library(questionr)
library(FactoMineR)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(psych)
## 
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha
## The following object is masked from 'package:questionr':
## 
##     describe
library(GPArotation)
library(FactoMineR)
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(explor)
options(max.print=5001)

data

df <- read.csv("./data/mjolnir_clean_v5_AMBI_BIG5.csv")

COV variables

df %>% dplyr::select(matches("^COV((ATT)|(CONSP)|(ORIGIN)|(POLITICS)|(COVERAGE)|(ANTIVACC)|(MEDSKEP))"))
##     COVATT_1 COVATT_2 COVATT_3 COVATT_4 COVATT_5 COVCONSP_1 COVCONSP_2
## 1          1        2        3        4        3          3          3
## 2          3        2        1        1        4          3          1
## 3          1        6        1        1        6          1          1
## 4          2        5        2        1        4          1          1
## 5          1        4        2        2        4          1          2
## 6          2        1        1        2        3          1          4
## 7          2        4        1        1        6          1          4
## 8          1        6        2        1        6          6          5
## 9          1        3        1        1        5          1          3
## 10         3        1        2        3        4          1          1
## 11         5        1        4        3        3          1          1
## 12         6        2        1        1        6          1          1
## 13         2        3        1        1        5          1          1
## 14         3        1        2        1        3          1          3
## 15         4        2        4        3        6          1          1
## 16         2        2        1        1        4          2          2
## 17         2        1        2        2        4          1          1
## 18         4        1        1        1        5          4          6
## 19         3        1        1        2        3          4          1
## 20         4        3        1        2        1          1          4
## 21         4        1        4        2        4          1          2
## 22         5        2        1        2        6          1          2
## 23         3        1        1        1        4          2          1
## 24         2        2        2        2        2          1          1
## 25         3        3        1        1        4          1          3
## 26         2        2        1        2        5          2          1
## 27         1        3        2        1        4          1          2
## 28         1        2        1        1        5          1          4
## 29         1        2        1        1        5          1          2
## 30         1        1        1        2        2          1          2
## 31         1        1        6        2        6          1          1
## 32         2        4        2        2        4          3          2
## 33         3        1        3        4        2          1          2
## 34         1        5        1        1        6          1          3
## 35         5        2        2        4        4          1          4
## 36         1        1        2        1        5          1          2
## 37         1        6        1        1        3          1          1
## 38         2        5        2        1        3          1          1
## 39         1        1        1        1        4          2          1
## 40         2        5        1        1        4          1          1
## 41         1        4        1        1        6          1          1
## 42         2        2        1        3        4          1          1
## 43         1        3        1        1        6          1          1
## 44         2        1        1        2        1          1          5
## 45         3        1        4        3        5          2          2
## 46         2        1        1        3        1          1          2
## 47         5        2        2        3        4          1          1
## 48         2        4        4        3        3          1          4
## 49         2        1        5        1        1          6          6
## 50         6        1        2        1        5          1          1
## 51         1        5        1        1        6          1          2
## 52         3        2        2        5        5          3          2
## 53         2        2        2        1        4          1          3
## 54         2        2        1        2        6          1          2
## 55         1        4        1        1        6          1          1
## 56         1        1        1        1        6          1          3
## 57         2        4        1        1        6          4          3
## 58         2        1        1        2        6          1          1
## 59         1        4        2        1        4          1          4
## 60         1        2        1        1        6          1          1
## 61         1        2        1        1        6          1          1
## 62         1        2        1        2        2          1          3
## 63         4        3        4        5        2          2          3
## 64         2        2        4        3        4          2          4
## 65         1        1        1        1        3          1          4
## 66         2        1        1        4        3          1          1
## 67         4        1        2        2        1          1          4
## 68         1        1        1        1        4          1          1
## 69         4        2        4        4        3          1          2
## 70         3        2        1        1        4          6          3
## 71         4        1        3        3        5          2          2
## 72         3        1        1        4        2          1          4
## 73         2        2        2        1        6          3          2
## 74         1        3        1        1        6          1          4
## 75         5        3        1        1        4          1          5
## 76         1        3        2        1        5          1          1
## 77         2        4        1        1        5          1          4
## 78         2        1        3        3        2          1          4
## 79         1        1        1        2        6          1          1
## 80         2        4        5        3        5          2          4
## 81         5        2        1        2        2          1          1
## 82         1        3        1        1        6          1          1
## 83         3        3        2        1        4          1          2
## 84         5        1        3        4        3          5          1
## 85         1        5        2        1        3          1          1
## 86         2        2        3        2        3          1          4
## 87         6        2        2        1        5          1          1
## 88         2        3        1        1        4          1          1
## 89         1        5        1        1        5          1          5
## 90         1        4        1        1        6          1          1
## 91         2        4        2        1        4          3          4
## 92         2        5        1        1        4          1          4
## 93         2        2        1        1        5          1          1
## 94         4        3        1        2        4          1          4
## 95         3        1        3        5        2          1          5
## 96         3        1        1        3        4          1          2
## 97         6        4        1        1        6          2          1
## 98         2        1        2        1        5          1          3
## 99         1        4        1        1        6          1          1
## 100        2        2        2        1        1          1          1
## 101        2        1        1        1        5          1          1
## 102        1        4        2        1        6          1          2
## 103        2        2        3        1        1          4          3
## 104        1        4        1        1        6          1          1
## 105        1        4        1        1        4          1          3
## 106        6        1        4        4        5          3          4
## 107        2        1        2        3        5          1          4
## 108        3        2        3        2        2          1          5
## 109        2        1        4        2        2          2          3
## 110        4        3        2        4        6          4          2
## 111        2        1        2        2        5          1          2
## 112        1        6        1        1        6          1          1
## 113        3        2        1        1        4          1          1
## 114        1        4        1        1        1          4          3
## 115        1        3        1        3        6          1          3
## 116        2        1        2        2        3          1          3
## 117        1        1        1        1        6          2          1
## 118        3        2        3        3        2          1          1
## 119        1        6        1        1        5          1          3
## 120        3        1        5        1        5          1          3
## 121        2        1        1        1        6          1          4
## 122        1        2        2        3        5          1          2
## 123        2        3        4        3        3          4          2
## 124        1        1        1        2        1          4          1
## 125        1        3        1        1        6          2          3
## 126        6        5        1        1        6          2          4
## 127        2        4        3        2        5          1          2
## 128        3        4        3        2        4          1          1
## 129        2        5        2        1        4          1          1
## 130        6        1        4        4        3          4          5
## 131        4        2        5        4        2          1          1
## 132        1        4        1        1        6          1          1
## 133        5        2        5        3        3          4          4
## 134        4        1        1        1        1          1          1
## 135        5        4        3        2        4          1          1
## 136        4        2        1        1        3          1          4
## 137        2        3        1        1        5          1          1
## 138        5        3        4        3        2          3          2
## 139        1        2        1        1        5          1          4
## 140        1        3        1        1        4          1          2
## 141        1        3        2        2        4          1          3
## 142        2        2        2        1        1          2          4
## 143        1        3        3        2        6          1          5
## 144        1        4        1        1        5          3          3
## 145        1        6        1        1        1          1          1
## 146        1        3        1        1        3          1          6
## 147        1        1        1        1        5          2          3
## 148        1        4        1        1        6          1          1
## 149        3        1        2        3        4          3          4
## 150        3        2        1        2        4          1          4
## 151        3        3        1        1        5          1          1
## 152        2        5        1        2        4          1          1
## 153        1        4        1        1        6          6          6
## 154        3        1        2        1        4          4          3
## 155        1        2        4        2        5          1          1
## 156        1        3        1        1        4          1          3
## 157        5        4        1        1        2          4          3
## 158        2        3        3        1        2          1          2
## 159        1        1        1        1        5          2          1
## 160        2        5        1        1        5          1          1
## 161        1        4        3        2        6          1          3
## 162        2        3        2        2        5          2          1
## 163        4        1        5        3        4          4          5
## 164        5        1        5        6        4          2          4
## 165        3        2        1        1        4          3          3
## 166        1        1        1        1        6          1          1
## 167        1        2        1        1        2          1          5
## 168        2        5        1        1        4          1          4
## 169        3        1        1        3        5          1          2
## 170        2        4        1        1        6          2          2
## 171        3        4        2        2        6          4          4
## 172        2        3        2        3        3          1          1
## 173        1        4        1        1        6          1          4
## 174        1        3        2        1        4          1          3
## 175        4        1        1        1        3          1          1
## 176        2        4        2        3        4          2          3
## 177        1        1        2        4        6          2          2
## 178        1        1        1        1        1          1          3
## 179        2        2        1        1        5          1          3
## 180        2        1        2        1        4          1          3
## 181        1        2        3        1        5          1          1
## 182        4        1        3        3        2          1          1
## 183        1        4        3        1        6          1          1
## 184        3        4        3        5        2          2          3
## 185        1        4        1        1        6          1          3
## 186        1        5        1        2        2          3          3
## 187        1        4        2        2        5          1          2
## 188        1        4        1        1        6          1          1
## 189        2        2        1        1        3          2          6
## 190        3        4        2        2        4          3          4
## 191        4        1        2        1        6          1          1
## 192        2        5        3        2        4          2          4
##     COVCONSP_3 COVCONSP_4 COVORIGIN_1 COVORIGIN_2 COVORIGIN_3 COVORIGIN_4
## 1            4          2           1           3           3           3
## 2            1          6           1           6           1           1
## 3            1          6           1           6           1           1
## 4            1          5           1           5           1           2
## 5            1          6           1           4           3           2
## 6            2          6           2           6           2           5
## 7            4          6           1           4           1           2
## 8            5          4           5           2           5           3
## 9            3          1           1           4           4           1
## 10           1          6           1           4           3           2
## 11           1          6           1           6           1           1
## 12           1          6           2           4           6           5
## 13           1          6           1           4           2           1
## 14           1          6           1           6           1           2
## 15           3          6           6           3           5           5
## 16           3          2           5           2           5           6
## 17           1          6           1           6           1           2
## 18           4          1           4           4           2           5
## 19           3          4           1           5           2           4
## 20           1          1           2           4           3           2
## 21           2          6           2           4           1           1
## 22           1          6           3           2           5           3
## 23           2          4           1           6           1           1
## 24           1          6           1           6           1           1
## 25           3          6           1           6           1           1
## 26           1          2           3           3           5           3
## 27           1          6           2           4           2           4
## 28           1          6           2           5           2           1
## 29           2          6           1           5           1           1
## 30           1          6           1           6           1           1
## 31           1          6           1           6           1           1
## 32           2          4           2           5           2           2
## 33           3          3           2           3           4           3
## 34           2          6           1           6           1           1
## 35           4          2           4           4           5           3
## 36           1          6           1           6           1           1
## 37           1          6           2           3           4           1
## 38           1          6           1           6           1           2
## 39           2          4           3           4           3           5
## 40           1          6           1           4           1           1
## 41           1          6           1           6           1           1
## 42           1          6           1           6           1           1
## 43           3          1           1           4           1           1
## 44           1          6           4           3           4           4
## 45           2          2           2           3           2           2
## 46           2          2           1           6           1           1
## 47           1          6           1           6           1           2
## 48           6          2           3           4           3           4
## 49           5          1           6           1           6           6
## 50           1          6           1           6           1           1
## 51           1          6           2           4           2           3
## 52           1          5           1           5           1           2
## 53           1          5           1           6           1           1
## 54           1          6           2           4           3           1
## 55           1          6           1           5           2           1
## 56           2          5           1           6           1           3
## 57           3          5           2           4           3           2
## 58           1          6           1           4           1           1
## 59           4          6           4           4           4           3
## 60           1          6           1           5           3           2
## 61           2          6           1           6           1           1
## 62           2          4           4           1           5           4
## 63           4          3           4           3           4           4
## 64           2          4           1           4           3           5
## 65           4          3           1           6           1           1
## 66           1          5           2           4           2           4
## 67           4          1           5           2           6           5
## 68           1          6           1           6           1           1
## 69           1          3           2           5           3           4
## 70           5          1           3           4           3           3
## 71           1          5           5           3           4           5
## 72           4          1           2           2           4           3
## 73           3          2           4           2           5           4
## 74           1          6           2           3           4           3
## 75           2          1           1           5           1           1
## 76           1          3           1           5           1           4
## 77           1          5           2           4           2           2
## 78           3          1           2           2           6           4
## 79           1          6           1           6           1           1
## 80           3          5           2           4           1           1
## 81           2          5           1           6           1           1
## 82           1          3           2           5           2           1
## 83           1          6           1           4           1           3
## 84           1          2           1           5           1           1
## 85           1          6           1           5           1           1
## 86           4          6           2           5           2           2
## 87           1          3           1           2           1           1
## 88           1          6           1           6           1           1
## 89           2          3           3           4           3           5
## 90           1          6           1           5           2           1
## 91           4          4           4           3           4           3
## 92           1          2           1           6           1           1
## 93           1          6           1           6           1           3
## 94           3          6           1           5           1           1
## 95           6          3           1           5           2           2
## 96           1          4           4           3           4           4
## 97           1          6           1           5           4           1
## 98           2          4           3           3           4           3
## 99           1          6           3           4           4           4
## 100          1          6           1           6           1           1
## 101          3          4           1           5           2           2
## 102          2          5           1           6           1           1
## 103          4          3           3           4           3           3
## 104          1          6           1           5           2           1
## 105          5          6           1           5           1           4
## 106          3          5           1           4           1           6
## 107          3          6           1           5           1           3
## 108          1          6           4           4           3           3
## 109          3          5           5           1           6           5
## 110          1          6           2           4           3           4
## 111          1          6           1           6           1           1
## 112          1          6           1           4           3           2
## 113          1          1           1           3           1           1
## 114          2          5           1           2           1           1
## 115          2          6           4           3           3           4
## 116          1          6           2           5           2           4
## 117          1          4           4           2           4           3
## 118          1          6           4           3           4           3
## 119          1          6           1           5           2           1
## 120          2          3           1           5           2           1
## 121          1          6           3           4           5           6
## 122          2          6           1           6           1           1
## 123          3          4           4           3           2           3
## 124          2          5           1           2           5           4
## 125          2          1           1           1           1           2
## 126          5          3           3           4           3           4
## 127          1          2           1           2           2           4
## 128          2          6           3           4           3           4
## 129          1          4           1           5           2           1
## 130          2          6           5           6           4           5
## 131          1          2           2           2           4           2
## 132          1          5           1           6           1           1
## 133          4          3           3           4           3           3
## 134          1          6           1           6           1           1
## 135          1          6           1           6           1           1
## 136          1          5           4           4           4           5
## 137          1          6           1           5           1           1
## 138          2          1           2           5           3           3
## 139          1          6           2           6           2           1
## 140          2          3           2           5           3           2
## 141          3          1           1           5           2           1
## 142          1          6           1           2           4           4
## 143          1          6           1           6           1           1
## 144          4          4           1           6           1           1
## 145          1          6           1           6           1           1
## 146          6          1           1           6           1           1
## 147          1          4           1           5           1           1
## 148          1          6           1           6           1           4
## 149          4          4           2           3           4           4
## 150          1          6           3           4           3           2
## 151          1          6           2           4           2           1
## 152          1          5           1           4           2           2
## 153          6          1           5           3           4           1
## 154          5          1           2           4           4           4
## 155          6          1           6           1           6           4
## 156          1          4           1           2           1           1
## 157          3          5           4           2           4           4
## 158          2          5           2           5           2           4
## 159          2          6           1           5           2           6
## 160          2          5           1           5           2           2
## 161          1          5           1           4           1           4
## 162          2          5           2           5           2           2
## 163          6          1           2           5           2           1
## 164          5          4           3           4           5           5
## 165          3          5           2           4           3           3
## 166          1          6           1           6           1           1
## 167          4          4           2           4           2           2
## 168          3          5           1           5           2           2
## 169          2          4           1           4           4           2
## 170          2          6           1           5           2           4
## 171          2          6           1           6           1           1
## 172          4          3           4           3           4           4
## 173          1          6           2           5           2           4
## 174          2          3           1           5           2           2
## 175          1          6           1           3           3           5
## 176          4          4           2           4           2           3
## 177          2          1           1           5           1           1
## 178          2          6           1           6           1           1
## 179          3          5           2           5           1           1
## 180          2          1           3           4           3           3
## 181          1          1           4           3           4           2
## 182          1          6           4           3           3           4
## 183          1          4           1           4           2           1
## 184          3          4           3           4           2           4
## 185          1          6           1           5           2           3
## 186          3          4           2           3           4           2
## 187          2          5           1           6           1           3
## 188          1          5           3           3           4           4
## 189          3          5           4           2           5           4
## 190          4          2           2           5           2           3
## 191          2          2           2           5           2           1
## 192          4          4           1           4           1           3
##     COVPOLITICS_1 COVPOLITICS_2 COVPOLITICS_3 COVCOVERAGE_1 COVCOVERAGE_2
## 1               2             2             3             2             4
## 2               6             1             6             1             6
## 3               1             1             6             5             2
## 4               1             1             1             1             3
## 5               3             3             4             4             4
## 6               6             5             2             6             3
## 7               3             3             5             2             1
## 8               6             2             3             3             5
## 9               1             1             1             1             4
## 10              6             3             3             4             4
## 11              1             2             5             2             5
## 12              1             1             6             1             3
## 13              2             1             6             2             2
## 14              4             2             6             2             4
## 15              4             4             4             6             4
## 16              6             3             5             4             3
## 17              1             1             4             3             2
## 18              6             2             4             3             6
## 19              4             5             6             4             6
## 20              6             4             6             6             2
## 21              4             3             3             4             4
## 22              6             3             5             4             2
## 23              6             1             6             3             4
## 24              6             2             6             4             1
## 25              3             1             6             2             5
## 26              4             2             5             2             2
## 27              4             2             6             3             4
## 28              1             1             5             1             2
## 29              3             2             5             3             4
## 30              1             1             6             1             5
## 31              5             1             5             5             4
## 32              3             3             5             3             4
## 33              3             3             4             3             5
## 34              1             1             6             1             4
## 35              6             2             2             4             4
## 36              1             1             6             1             4
## 37              1             1             6             1             5
## 38              4             1             6             2             4
## 39              5             2             5             3             4
## 40              2             2             6             3             4
## 41              1             1             6             3             2
## 42              6             2             6             4             3
## 43              1             1             4             1             4
## 44              4             3             5             3             4
## 45              4             4             2             2             2
## 46              6             1             6             1             3
## 47              4             2             5             4             3
## 48              6             4             6             4             4
## 49              6             4             6             4             5
## 50              5             1             6             1             4
## 51              4             1             6             1             2
## 52              4             3             2             4             3
## 53              1             1             6             1             4
## 54              5             1             5             2             4
## 55              1             1             6             4             3
## 56              1             1             4             1             4
## 57              3             1             5             2             5
## 58              2             1             5             1             5
## 59              3             2             4             2             4
## 60              4             4             3             4             5
## 61              1             1             6             1             6
## 62              4             4             5             4             2
## 63              3             6             3             6             4
## 64              4             3             4             3             4
## 65              4             4             5             4             3
## 66              5             3             6             2             6
## 67              4             5             2             5             2
## 68              2             1             6             3             4
## 69              4             5             3             6             3
## 70              5             5             4             5             4
## 71              6             2             4             3             2
## 72              6             4             4             6             2
## 73              5             1             5             2             4
## 74              3             1             6             3             4
## 75              2             2             5             3             4
## 76              6             3             6             3             4
## 77              4             3             6             4             3
## 78              6             6             2             6             3
## 79              1             1             6             1             5
## 80              1             1             1             5             2
## 81              4             3             5             3             6
## 82              1             1             6             2             5
## 83              5             4             6             5             5
## 84              1             4             3             3             3
## 85              1             1             6             1             1
## 86              4             3             5             3             3
## 87              1             4             6             2             4
## 88              5             1             6             4             5
## 89              4             3             5             4             2
## 90              3             1             6             1             2
## 91              4             2             5             3             4
## 92              4             1             4             1             5
## 93              4             3             4             4             5
## 94              4             2             5             6             3
## 95              6             5             2             6             4
## 96              5             3             5             3             3
## 97              5             1             6             1             3
## 98              4             4             5             4             4
## 99              1             1             6             1             3
## 100             4             2             4             4             4
## 101             1             1             5             1             4
## 102             1             1             6             1             5
## 103             3             2             5             3             5
## 104             1             1             6             1             3
## 105             4             1             6             3             5
## 106             6             3             5             4             4
## 107             4             4             6             5             5
## 108             5             4             2             4             2
## 109             5             6             3             3             4
## 110             6             1             6             3             4
## 111             2             2             5             2             3
## 112             1             1             6             1             4
## 113             4             2             6             4             3
## 114             4             1             6             1             3
## 115             4             1             6             2             5
## 116             2             1             5             2             2
## 117             6             1             5             4             4
## 118             4             4             3             5             4
## 119             1             1             6             1             4
## 120             4             3             6             3             3
## 121             6             4             5             6             4
## 122             4             1             6             2             2
## 123             3             5             3             4             2
## 124             6             2             5             2             4
## 125             6             1             6             2             4
## 126             3             1             6             3             4
## 127             6             2             5             6             5
## 128             4             5             6             5             3
## 129             6             1             5             2             3
## 130             6             6             4             6             5
## 131             5             5             2             5             4
## 132             2             1             6             3             2
## 133             4             5             3             5             3
## 134             6             1             6             5             3
## 135             5             2             6             4             3
## 136             6             3             6             5             3
## 137             2             2             6             2             3
## 138             5             4             4             4             2
## 139             1             1             6             1             6
## 140             2             2             5             2             2
## 141             4             3             6             3             4
## 142             3             4             5             4             3
## 143             6             1             6             1             2
## 144             2             2             6             2             5
## 145             1             1             6             1             6
## 146             4             1             6             1             2
## 147             4             2             5             5             4
## 148             4             2             6             3             5
## 149             6             4             4             4             3
## 150             4             2             5             2             4
## 151             4             3             5             4             4
## 152             5             2             5             2             3
## 153             1             1             6             1             1
## 154             6             6             6             6             1
## 155             3             2             4             2             1
## 156             2             1             4             2             4
## 157             3             2             4             2             2
## 158             2             2             5             2             4
## 159             4             1             6             1             5
## 160             4             1             6             3             4
## 161             6             4             4             5             6
## 162             2             2             5             2             5
## 163             6             6             1             6             1
## 164             4             6             4             6             3
## 165             4             3             4             4             3
## 166             1             1             6             1             6
## 167             3             1             6             1             3
## 168             2             2             5             2             4
## 169             3             2             6             4             4
## 170             3             2             5             3             4
## 171             6             2             5             3             4
## 172             6             4             5             4             2
## 173             3             3             6             2             5
## 174             3             2             6             2             4
## 175             5             4             4             4             3
## 176             4             4             4             4             4
## 177             6             3             5             4             3
## 178             6             3             6             6             5
## 179             1             2             5             4             4
## 180             4             2             6             3             5
## 181             2             2             5             3             5
## 182             5             6             4             5             3
## 183             6             1             5             3             4
## 184             4             4             4             4             3
## 185             1             1             6             2             2
## 186             4             2             5             3             4
## 187             4             1             6             2             4
## 188             4             1             6             1             3
## 189             4             1             6             2             5
## 190             2             2             4             3             4
## 191             4             4             6             4             5
## 192             4             2             6             4             4
##     COVCOVERAGE_3 COVANTIVACC_1 COVANTIVACC_2 COVANTIVACC_3 COVMEDSKEP_1
## 1               4             3             3             4            4
## 2               1             3             1             6            1
## 3               1             1             1             6            1
## 4               3             1             2             5            1
## 5               3             1             1             6            2
## 6               1             5             3             4            2
## 7               2             2             2             5            1
## 8               3             5             1             3            3
## 9               1             1             1             6            3
## 10              4             5             3             3            3
## 11              3             3             1             3            2
## 12              2             1             1             6            5
## 13              1             2             1             6            1
## 14              2             1             2             4            1
## 15              4             4             5             1            5
## 16              5             6             4             3            5
## 17              3             1             1             6            1
## 18              3             1             1             6            4
## 19              3             4             1             6            3
## 20              3             5             2             5            6
## 21              3             3             1             4            1
## 22              5             2             2             6            2
## 23              3             2             1             5            1
## 24              1             1             1             6            1
## 25              1             1             1             6            1
## 26              1             2             1             5            3
## 27              2             1             1             6            3
## 28              1             1             2             4            2
## 29              3             1             1             6            1
## 30              1             1             1             6            1
## 31              2             1             1             6            1
## 32              3             3             1             5            1
## 33              3             2             1             6            1
## 34              1             1             1             6            1
## 35              3             6             4             3            6
## 36              3             1             1             6            1
## 37              1             1             1             5            1
## 38              1             1             1             6            1
## 39              2             5             1             6            2
## 40              3             2             1             5            1
## 41              1             1             1             6            2
## 42              1             1             1             4            2
## 43              1             1             1             4            3
## 44              2             2             2             5            3
## 45              2             3             4             4            5
## 46              1             1             1             6            1
## 47              1             4             1             5            3
## 48              3             4             2             5            3
## 49              4             6             4             3            4
## 50              2             1             1             6            1
## 51              1             2             1             6            1
## 52              3             3             1             4            4
## 53              1             1             1             6            1
## 54              4             2             1             5            1
## 55              1             1             1             6            2
## 56              3             1             1             6            1
## 57              3             2             1             6            1
## 58              1             1             1             5            1
## 59              2             2             2             5            1
## 60              1             3             1             6            1
## 61              1             1             1             6            1
## 62              3             6             3             2            2
## 63              3             4             3             3            6
## 64              2             2             1             5            2
## 65              1             1             1             5            3
## 66              1             5             1             6            3
## 67              4             3             1             2            4
## 68              2             1             1             6            1
## 69              4             3             2             5            3
## 70              4             5             1             5            3
## 71              3             2             1             3            2
## 72              5             5             2             4            4
## 73              2             5             2             5            4
## 74              4             4             3             4            2
## 75              4             1             1             6            1
## 76              4             4             4             6            3
## 77              2             2             1             6            1
## 78              4             2             1             6            5
## 79              1             1             1             6            1
## 80              3             2             1             5            1
## 81              1             2             1             6            6
## 82              1             1             1             6            5
## 83              2             1             1             6            1
## 84              4             1             1             6            3
## 85              1             1             1             6            1
## 86              3             1             1             5            3
## 87              1             1             1             5            2
## 88              4             1             1             6            1
## 89              4             4             2             3            4
## 90              1             1             1             6            1
## 91              3             3             1             5            3
## 92              2             1             1             6            1
## 93              3             4             3             2            2
## 94              3             2             2             4            4
## 95              6             2             1             6            4
## 96              4             2             1             3            3
## 97              2             1             1             6            3
## 98              3             3             2             5            3
## 99              1             3             1             4            1
## 100             4             2             2             4            1
## 101             3             1             1             6            1
## 102             1             1             1             6            1
## 103             2             2             1             6            1
## 104             1             3             1             5            1
## 105             1             4             1             6            1
## 106             2             2             1             4            4
## 107             6             1             2             5            4
## 108             3             4             1             3            3
## 109             3             6             6             2            6
## 110             2             2             1             5            2
## 111             1             1             1             5            1
## 112             1             1             1             6            1
## 113             2             1             1             6            1
## 114             5             2             1             6            1
## 115             1             1             1             6            2
## 116             1             2             1             6            1
## 117             4             2             1             6            1
## 118             3             4             1             6            3
## 119             1             1             1             6            1
## 120             2             2             1             6            1
## 121             4             4             1             6            4
## 122             1             1             1             6            1
## 123             3             4             2             3            3
## 124             4             5             1             6            4
## 125             1             1             1             6            1
## 126             2             3             3             4            3
## 127             6             1             1             5            2
## 128             5             5             4             3            4
## 129             4             1             1             5            1
## 130             3             6             3             4            2
## 131             3             1             1             6            3
## 132             1             1             1             6            1
## 133             4             3             3             4            5
## 134             1             1             1             6            3
## 135             2             1             1             6            1
## 136             2             1             1             5            2
## 137             2             2             1             6            1
## 138             4             5             3             2            2
## 139             1             1             1             4            2
## 140             2             1             1             6            1
## 141             3             2             2             5            2
## 142             4             5             1             5            2
## 143             1             4             1             6            1
## 144             1             2             1             6            2
## 145             1             2             1             4            1
## 146             1             1             1             5            1
## 147             3             2             1             2            1
## 148             2             2             1             5            4
## 149             3             2             2             5            2
## 150             2             1             1             6            2
## 151             3             2             1             5            1
## 152             1             1             1             5            1
## 153             1             4             1             6            5
## 154             6             4             1             4            4
## 155             6             1             1             4            3
## 156             1             1             1             5            1
## 157             3             5             5             3            3
## 158             2             1             1             5            1
## 159             3             1             1             6            1
## 160             2             2             1             6            3
## 161             1             1             2             4            1
## 162             2             2             2             5            4
## 163             6             1             1             6            4
## 164             4             5             5             4            6
## 165             3             1             1             5            2
## 166             1             1             1             6            1
## 167             3             2             1             6            1
## 168             1             1             2             4            3
## 169             3             4             1             6            3
## 170             3             3             1             5            2
## 171             2             3             1             6            1
## 172             5             5             2             5            5
## 173             3             2             1             6            1
## 174             1             1             1             6            2
## 175             3             1             1             5            5
## 176             5             3             3             4            3
## 177             2             1             1             6            1
## 178             4             2             1             6            1
## 179             4             1             1             5            2
## 180             3             2             1             4            2
## 181             4             5             2             4            4
## 182             3             2             3             2            3
## 183             3             2             1             6            3
## 184             4             4             4             3            4
## 185             2             1             1             5            4
## 186             3             3             2             5            4
## 187             5             1             1             6            1
## 188             2             1             1             6            3
## 189             2             4             1             6            1
## 190             4             2             2             4            3
## 191             4             1             1             6            2
## 192             2             5             3             4            3
##     COVMEDSKEP_2 COVMEDSKEP_3 COVMEDSKEP_4
## 1              3            4            4
## 2              1            6            6
## 3              1            6            6
## 4              2            1            3
## 5              1            6            6
## 6              2            5            5
## 7              2            5            5
## 8              4            5            5
## 9              1            4            5
## 10             3            3            3
## 11             1            5            5
## 12             4            5            5
## 13             1            6            6
## 14             1            6            6
## 15             5            4            3
## 16             3            3            4
## 17             1            6            6
## 18             5            3            4
## 19             1            4            6
## 20             3            4            4
## 21             1            6            6
## 22             2            5            6
## 23             1            6            6
## 24             1            6            6
## 25             1            6            6
## 26             2            5            5
## 27             2            4            6
## 28             1            5            5
## 29             1            6            6
## 30             1            5            6
## 31             1            6            6
## 32             2            4            4
## 33             1            6            5
## 34             1            5            6
## 35             6            2            2
## 36             1            6            6
## 37             1            6            6
## 38             1            5            6
## 39             1            6            6
## 40             1            6            6
## 41             1            5            6
## 42             2            5            5
## 43             2            4            6
## 44             4            4            4
## 45             3            3            4
## 46             1            6            6
## 47             2            4            5
## 48             3            4            4
## 49             4            4            4
## 50             1            6            6
## 51             1            6            6
## 52             2            2            3
## 53             1            6            6
## 54             1            5            6
## 55             1            5            5
## 56             1            2            6
## 57             2            6            6
## 58             1            5            6
## 59             1            2            6
## 60             1            6            6
## 61             1            6            6
## 62             2            4            4
## 63             4            2            3
## 64             2            3            5
## 65             1            5            5
## 66             5            5            6
## 67             4            2            3
## 68             1            6            6
## 69             4            4            4
## 70             2            5            6
## 71             2            4            4
## 72             3            3            3
## 73             3            3            4
## 74             2            5            5
## 75             1            6            6
## 76             1            5            6
## 77             1            6            6
## 78             4            3            2
## 79             1            6            6
## 80             1            5            5
## 81             2            6            6
## 82             1            6            6
## 83             2            5            6
## 84             1            5            6
## 85             1            4            5
## 86             2            5            5
## 87             1            6            6
## 88             1            4            6
## 89             4            3            4
## 90             1            6            6
## 91             4            4            4
## 92             1            6            6
## 93             2            6            6
## 94             2            3            3
## 95             2            4            4
## 96             3            4            4
## 97             3            4            6
## 98             3            5            5
## 99             1            6            6
## 100            1            2            5
## 101            2            5            6
## 102            1            6            6
## 103            1            6            6
## 104            1            6            6
## 105            1            6            6
## 106            4            4            4
## 107            2            5            5
## 108            4            4            4
## 109            6            6            4
## 110            3            5            5
## 111            1            6            6
## 112            4            6            6
## 113            1            6            6
## 114            2            5            5
## 115            2            5            6
## 116            1            5            6
## 117            2            5            5
## 118            3            4            4
## 119            2            6            6
## 120            1            6            6
## 121            4            4            5
## 122            1            5            5
## 123            2            4            3
## 124            2            3            6
## 125            1            5            6
## 126            4            4            4
## 127            3            4            5
## 128            3            4            5
## 129            1            6            5
## 130            3            5            5
## 131            1            4            5
## 132            1            6            6
## 133            4            3            3
## 134            1            4            6
## 135            1            6            6
## 136            2            5            6
## 137            1            5            5
## 138            2            4            4
## 139            1            6            6
## 140            2            5            6
## 141            1            5            5
## 142            2            4            5
## 143            1            6            6
## 144            2            5            5
## 145            2            6            6
## 146            1            3            6
## 147            2            5            4
## 148            5            4            5
## 149            2            3            6
## 150            2            5            6
## 151            1            6            6
## 152            1            5            5
## 153            4            4            6
## 154            3            2            4
## 155            4            3            3
## 156            1            4            6
## 157            5            4            4
## 158            2            4            6
## 159            1            6            6
## 160            2            4            4
## 161            2            6            6
## 162            2            3            3
## 163            2            5            4
## 164            5            4            5
## 165            3            5            5
## 166            1            6            6
## 167            1            6            6
## 168            3            4            5
## 169            2            5            6
## 170            2            5            5
## 171            1            6            6
## 172            5            2            4
## 173            1            5            5
## 174            2            6            5
## 175            4            4            4
## 176            3            4            4
## 177            1            5            6
## 178            1            6            1
## 179            2            4            4
## 180            2            3            6
## 181            3            3            5
## 182            2            4            4
## 183            1            4            6
## 184            4            3            3
## 185            3            4            5
## 186            4            4            4
## 187            1            6            6
## 188            3            3            5
## 189            2            4            6
## 190            3            4            4
## 191            1            5            5
## 192            4            4            5
##  [ reached 'max' / getOption("max.print") -- omitted 470 rows ]
df_covmis <- df %>%
  mutate(covmis_att_flu=COVATT_1,
         covmis_att_afrDie=7-COVATT_2,
         covmis_att_eldrNoBgDl=COVATT_3,
         covmis_att_rareNoWorr=COVATT_4,
         covmis_att_bgThrt=7-COVATT_5,
         covmis_cnsp_ctiusAsian=COVCONSP_1,
         covmis_cnsp_stpCovStpImmi=COVCONSP_2,
         covmis_cnsp_redIntWthChina=COVCONSP_3,
         covmis_cnsp_chnsCovRcst=7-COVCONSP_4,
         covmis_orgn_covPlnnd=COVORIGIN_1,
         covmis_orgn_covNat=7-COVORIGIN_2,
         covmis_orgn_covNgeenLab=COVORIGIN_3,
         covmis_orgn_scntFkNwsCov=COVORIGIN_4,
         covmis_pltc_polBgDlIntrst=COVPOLITICS_1,
         covmis_pltc_covNtSerPolSay=COVPOLITICS_2,
         covmis_pltc_polDwnplCovPlpLDngr=7-COVPOLITICS_3,
         covmis_cvrg_mdiaCovBgrDl=COVCOVERAGE_1,
         covmis_cvrg_nwsGdJbComCov=7-COVCOVERAGE_2,
         covmis_cvrg_mdiaUseCovMkTrmpRepLkBd=COVCOVERAGE_3,
         covmis_anti_frGovUseCovMndtVacc=COVANTIVACC_1,
         covmis_anti_thnksNoCovVacc=COVANTIVACC_2,
         covmis_anti_covVacEffRedVirus=7-COVANTIVACC_3,
         covmis_mdsk_medOrgUntrust=COVMEDSKEP_1,
         covmis_mdsk_skeptInfoDocSci=COVMEDSKEP_2,
         covmis_mdsk_medOrgRecBstInt=7-COVMEDSKEP_3,
         covmis_mdsk_fllwRecMedOrgImp=7-COVMEDSKEP_4)

PCA analysis

res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_att")),
               scale.unit=TRUE, ncp=25, graph=T)

#explor(res.pca)
res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_cnsp")),
               scale.unit=TRUE, ncp=25, graph=T)

#explor(res.pca)
res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_orgn")),
               scale.unit=TRUE, ncp=25, graph=T)

#explor(res.pca)
res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_pltc")),
               scale.unit=TRUE, ncp=25, graph=T)

#explor(res.pca)
res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_cvrg")),
               scale.unit=TRUE, ncp=25, graph=T)

#explor(res.pca)
res.pca <- PCA(df_covmis %>% dplyr::select(matches("^covmis_anti")),
               scale.unit=TRUE, ncp=25, graph=T)

#explor(res.pca)

HCPM ET CLASSIF

res.mfa <- MFA(df_covmis %>% dplyr::select(matches("^covmis")) %>% rename_with(function(x){str_remove(x,"^covmis_[A-z]{3,4}_")}),
               group = c(5,4,4,3,3,3,4),
               type=c("c","c","c","c","c","c","c"),
               name.group = c("att","conspiracy","origin","politics","coverage","antivaccin", "Mdsk"))

## Warning: ggrepel: 655 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 608 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

get_eigenvalue(res.mfa) %>% head
##       eigenvalue variance.percent cumulative.variance.percent
## Dim.1  4.1453673        37.179358                    37.17936
## Dim.2  0.9929282         8.905467                    46.08482
## Dim.3  0.6763359         6.065985                    52.15081
## Dim.4  0.5696746         5.109351                    57.26016
## Dim.5  0.5318857         4.770426                    62.03059
## Dim.6  0.4620883         4.144421                    66.17501
fviz_screeplot(res.mfa)

group <- get_mfa_var(res.mfa, "group")

group$coord
##                Dim.1       Dim.2      Dim.3       Dim.4       Dim.5
## att        0.4288261 0.400972540 0.02252065 0.117682630 0.332010787
## conspiracy 0.3687215 0.240858293 0.33687679 0.052382138 0.113129399
## origin     0.6423536 0.102705673 0.04241034 0.009136517 0.005009324
## politics   0.7064960 0.091408058 0.02478238 0.302679657 0.006263261
## coverage   0.7110741 0.063562788 0.04632540 0.041165098 0.032486902
## antivaccin 0.6358459 0.005753999 0.17000533 0.027384851 0.007699764
## Mdsk       0.6520501 0.087666845 0.03341500 0.019243732 0.035286238
group$contrib
##                Dim.1     Dim.2     Dim.3     Dim.4      Dim.5
## att        10.344708 40.382834  3.329803 20.657868 62.4214568
## conspiracy  8.894784 24.257373 49.809095  9.195098 21.2694953
## origin     15.495697 10.343716  6.270603  1.603813  0.9418047
## politics   17.043025  9.205908  3.664212 53.132024  1.1775578
## coverage   17.153465  6.401549  6.849467  7.226072  6.1078731
## antivaccin 15.338711  0.579498 25.136228  4.807104  1.4476352
## Mdsk       15.729609  8.829122  4.940592  3.378022  6.6341771
# Contribution to the first dimension
fviz_contrib(res.mfa, "group", axes = 1)

# Contribution to the second dimension
fviz_contrib(res.mfa, "group", axes = 2)

quanti.var <- get_mfa_var(res.mfa, "quanti.var")
quanti.var$contrib
##                             Dim.1       Dim.2       Dim.3        Dim.4
## flu                     2.6064772  7.73560847  0.03709541 3.835763e+00
## afrDie                  1.4959458 19.85699962  2.95902165 5.792656e+00
## eldrNoBgDl              1.6922641  1.67308477  0.03383058 2.854280e+00
## rareNoWorr              2.1734152  2.34024908  0.01092408 2.044521e+00
## bgThrt                  2.3766055  8.77689160  0.28893155 6.130648e+00
## ctiusAsian              0.4727276  2.49084690  1.75270757 3.877927e-04
## stpCovStpImmi           1.7992285  6.00949577 14.24358834 7.331952e-02
## redIntWthChina          2.4372337 11.78788718 11.73464218 9.316842e-01
## chnsCovRcst             4.1855945  3.96914287 22.07815712 8.189706e+00
## covPlnnd                3.2349186  2.17350639  1.55211840 2.988802e-02
## covNat                  3.0805566  4.32929552  1.28388754 3.772871e-06
## covNgeenLab             4.5029796  3.57720223  1.45014403 3.008399e-01
## scntFkNwsCov            4.6772426  0.26371186  1.98445253 1.273082e+00
## polBgDlIntrst           7.2656494  4.03509071  3.32272405 4.793314e+01
## covNtSerPolSay          6.4381947  3.51677321  0.30856562 1.018422e-01
## polDwnplCovPlpLDngr     3.3391812  1.65404422  0.03292261 5.097044e+00
## mdiaCovBgrDl            9.1007133  6.21123408  2.26851047 3.281355e+00
## nwsGdJbComCov           0.9491219  0.02372072  0.64815352 8.603278e-02
## mdiaUseCovMkTrmpRepLkBd 7.1036297  0.16659452  3.93280255 3.858684e+00
## frGovUseCovMndtVacc     7.8442930  0.23524817 10.16925338 2.079372e+00
## thnksNoCovVacc          2.9838512  0.22695275  5.74970514 2.579411e-01
## covVacEffRedVirus       4.5105670  0.11729709  9.21726956 2.469791e+00
## medOrgUntrust           4.7580788  3.99357954  1.01615181 6.491236e-01
## skeptInfoDocSci         4.1506347  1.73385579  2.23444151 1.448116e-03
## medOrgRecBstInt         3.5734929  2.12130754  0.83305724 9.003195e-01
## fllwRecMedOrgImp        3.2474025  0.98037941  0.85694156 1.827130e+00
##                                Dim.5
## flu                     4.190999e+01
## afrDie                  1.271874e+01
## eldrNoBgDl              2.697937e+00
## rareNoWorr              2.023975e+00
## bgThrt                  3.070812e+00
## ctiusAsian              2.742445e+00
## stpCovStpImmi           5.901999e+00
## redIntWthChina          4.143984e+00
## chnsCovRcst             8.481068e+00
## covPlnnd                2.670275e-02
## covNat                  2.664329e-01
## covNgeenLab             5.575524e-01
## scntFkNwsCov            9.111669e-02
## polBgDlIntrst           2.780905e-01
## covNtSerPolSay          7.117344e-01
## polDwnplCovPlpLDngr     1.877329e-01
## mdiaCovBgrDl            3.315568e-04
## nwsGdJbComCov           5.875296e+00
## mdiaUseCovMkTrmpRepLkBd 2.322459e-01
## frGovUseCovMndtVacc     5.437454e-01
## thnksNoCovVacc          8.992008e-01
## covVacEffRedVirus       4.689018e-03
## medOrgUntrust           2.072189e+00
## skeptInfoDocSci         1.833596e-02
## medOrgRecBstInt         3.693040e+00
## fllwRecMedOrgImp        8.506115e-01
quanti.var$coord
##                             Dim.1       Dim.2       Dim.3         Dim.4
## flu                     0.6527693 -0.55037335 -0.03145520  0.2935557090
## afrDie                  0.4945278 -0.88179366  0.28093544  0.3607476911
## eldrNoBgDl              0.5259771 -0.25595825  0.03003912  0.2532287182
## rareNoWorr              0.5960794 -0.30272006  0.01706966  0.2143188992
## bgThrt                  0.6233203 -0.58624685 -0.08778695  0.3711229863
## ctiusAsian              0.3054965  0.34320347  0.23760479  0.0032436390
## stpCovStpImmi           0.5959968  0.53308587  0.67734536  0.0446007833
## redIntWthChina          0.6936643  0.74661402  0.61480215  0.1589889850
## chnsCovRcst             0.9090323  0.43323798  0.84329954  0.4713753081
## covPlnnd                0.8207190  0.32924614 -0.22962806 -0.0292444414
## covNat                  0.8008984  0.46467450 -0.20884593  0.0003285721
## covNgeenLab             0.9683064  0.42238839 -0.22195662 -0.0927817353
## scntFkNwsCov            0.9868650  0.11468459 -0.25964674 -0.1908634880
## polBgDlIntrst           1.1501782 -0.41950000  0.31417726 -1.0951604768
## covNtSerPolSay          1.0827045 -0.39163139  0.09574175  0.0504805096
## polDwnplCovPlpLDngr     0.7797366 -0.26858322  0.03127337  0.3571239876
## mdiaCovBgrDl            1.2257469 -0.49559791  0.24719131 -0.2728485478
## nwsGdJbComCov           0.3958440 -0.03062700 -0.13213010  0.0441801322
## mdiaUseCovMkTrmpRepLkBd 1.0829372 -0.08116539  0.32547219 -0.2958793399
## frGovUseCovMndtVacc     1.0549893  0.08941527 -0.48519398 -0.2013581254
## thnksNoCovVacc          0.6506684  0.08782462 -0.36483261  0.0709191191
## covVacEffRedVirus       0.7999940  0.06313820 -0.46192558  0.2194487867
## medOrgUntrust           0.9729381  0.43624251 -0.18161362  0.1332186491
## skeptInfoDocSci         0.9087128  0.28744403 -0.26931070 -0.0062922075
## medOrgRecBstInt         0.8431714  0.31794253 -0.16443965  0.1568915898
## fllwRecMedOrgImp        0.8037805  0.21614436 -0.16678029  0.2235044113
##                                Dim.5
## flu                      0.937603389
## afrDie                  -0.516514418
## eldrNoBgDl               0.237890057
## rareNoWorr               0.206045408
## bgThrt                  -0.253797318
## ctiusAsian               0.263570819
## stpCovStpImmi            0.386658854
## redIntWthChina           0.323994317
## chnsCovRcst             -0.463504171
## covPlnnd                 0.026709667
## covNat                  -0.084369286
## covNgeenLab             -0.122048737
## scntFkNwsCov            -0.049338894
## polBgDlIntrst           -0.080602515
## covNtSerPolSay           0.128947976
## polDwnplCovPlpLDngr      0.066225574
## mdiaCovBgrDl             0.002650145
## nwsGdJbComCov           -0.352781339
## mdiaUseCovMkTrmpRepLkBd -0.070139844
## frGovUseCovMndtVacc      0.099493929
## thnksNoCovVacc           0.127946127
## covVacEffRedVirus        0.009239314
## medOrgUntrust           -0.229991477
## skeptInfoDocSci          0.021634601
## medOrgRecBstInt         -0.307035835
## fllwRecMedOrgImp        -0.147354247
fviz_mfa_var(res.mfa, "quanti.var", palette = "jco", 
             col.var.sup = "violet", repel = TRUE)

fviz_mfa_var(res.mfa, "quanti.var", palette = "jco", 
             col.var.sup = "violet", repel = TRUE,
             geom = c("point", "text"), legend = "bottom")
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

# Contributions to dimension 1
fviz_contrib(res.mfa, choice = "quanti.var", axes = 1, top = 20,
             palette = "jco")

# Contributions to dimension 2
fviz_contrib(res.mfa, choice = "quanti.var", axes = 2, top = 20,
             palette = "jco")

Classification

res.hcpc <- HCPC(res.mfa, nb.clust=4, graph = T)

fviz_cluster(res.hcpc,
             repel = TRUE, 
             show.clust.cent = TRUE, 
             palette = "jco", 
             ggtheme = theme_minimal(),
             main = "Factor map"
             )
## Warning: ggrepel: 601 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

res.hcpc$desc.var$quanti
## $`1`
##                             v.test Mean in category Overall mean sd in category
## nwsGdJbComCov            -4.015687         3.007117     3.235650      1.2370119
## afrDie                   -5.239376         3.583630     3.950151      1.5259146
## flu                      -7.166642         1.722420     2.214502      1.4045538
## ctiusAsian               -7.735925         1.153025     1.510574      0.4861788
## bgThrt                   -8.082505         1.939502     2.474320      1.1597821
## thnksNoCovVacc           -8.859401         1.049822     1.451662      0.3643475
## eldrNoBgDl               -8.970743         1.288256     1.712991      0.5773027
## stpCovStpImmi            -9.945192         1.640569     2.291541      1.1011116
## covNat                  -10.021669         1.932384     2.569486      1.1836900
## covVacEffRedVirus       -10.345263         1.398577     1.962236      0.6784938
## redIntWthChina          -10.363849         1.494662     2.175227      0.9012486
## rareNoWorr              -10.683372         1.138790     1.623867      0.3558719
## chnsCovRcst             -10.936419         1.530249     2.377644      1.0637891
## polDwnplCovPlpLDngr     -10.996744         1.249110     1.853474      0.6604277
## fllwRecMedOrgImp        -12.016231         1.291815     1.903323      0.5661607
## covPlnnd                -12.206609         1.131673     1.773414      0.3871978
## medOrgUntrust           -12.409667         1.419929     2.222054      0.8057807
## medOrgRecBstInt         -12.468806         1.494662     2.217523      0.8008936
## covNgeenLab             -12.511153         1.402135     2.203927      0.7097659
## skeptInfoDocSci         -12.649127         1.213523     1.906344      0.5820219
## scntFkNwsCov            -12.902313         1.259786     2.054381      0.7209019
## frGovUseCovMndtVacc     -13.013190         1.202847     2.045317      0.5578228
## covNtSerPolSay          -14.654241         1.163701     2.074018      0.5007814
## mdiaUseCovMkTrmpRepLkBd -15.756154         1.338078     2.413897      0.7130770
## polBgDlIntrst           -16.435821         1.950178     3.288520      1.3827901
## mdiaCovBgrDl            -17.020175         1.423488     2.649547      0.6921136
##                         Overall sd      p.value
## nwsGdJbComCov             1.256547 5.927280e-05
## afrDie                    1.544582 1.611204e-07
## flu                       1.516047 7.685970e-13
## ctiusAsian                1.020504 1.026537e-14
## bgThrt                    1.461005 6.344998e-16
## thnksNoCovVacc            1.001474 8.044537e-19
## eldrNoBgDl                1.045396 2.945216e-19
## stpCovStpImmi             1.445240 2.646619e-23
## covNat                    1.403655 1.224179e-23
## covVacEffRedVirus         1.203002 4.397085e-25
## redIntWthChina            1.449906 3.620910e-25
## rareNoWorr                1.002522 1.217668e-26
## chnsCovRcst               1.710810 7.718886e-28
## polDwnplCovPlpLDngr       1.213461 3.961795e-28
## fllwRecMedOrgImp          1.123636 2.919872e-33
## covPlnnd                  1.160797 2.865882e-34
## medOrgUntrust             1.427163 2.316130e-35
## medOrgRecBstInt           1.280033 1.104688e-35
## covNgeenLab               1.414998 6.487553e-36
## skeptInfoDocSci           1.209350 1.131243e-36
## scntFkNwsCov              1.359782 4.367880e-38
## frGovUseCovMndtVacc       1.429428 1.029526e-38
## covNtSerPolSay            1.371579 1.265626e-48
## mdiaUseCovMkTrmpRepLkBd   1.507580 6.231180e-56
## polBgDlIntrst             1.797906 1.059796e-60
## mdiaCovBgrDl              1.590519 5.819639e-65
## 
## $`2`
##                            v.test Mean in category Overall mean sd in category
## redIntWthChina           8.381118         3.140625     2.175227      1.5498708
## ctiusAsian               6.807711         2.062500     1.510574      1.4017288
## stpCovStpImmi            6.714696         3.062500     2.291541      1.5799031
## covNgeenLab              5.274671         2.796875     2.203927      1.3883408
## medOrgRecBstInt          5.005710         2.726562     2.217523      1.2100803
## medOrgUntrust            4.656404         2.750000     2.222054      1.4142136
## covNat                   4.631311         3.085938     2.569486      1.1860343
## chnsCovRcst              4.521538         2.992188     2.377644      1.8477017
## skeptInfoDocSci          4.146105         2.304688     1.906344      1.1219305
## covPlnnd                 3.897222         2.132812     1.773414      1.1065931
## frGovUseCovMndtVacc      2.834352         2.367188     2.045317      1.3102427
## polBgDlIntrst            2.793313         3.687500     3.288520      1.4724024
## scntFkNwsCov             2.534026         2.328125     2.054381      1.1395543
## mdiaUseCovMkTrmpRepLkBd  2.284415         2.687500     2.413897      1.3448397
## fllwRecMedOrgImp         2.133229         2.093750     1.903323      0.8964993
## eldrNoBgDl              -2.376438         1.515625     1.712991      0.8290090
## polDwnplCovPlpLDngr     -2.694150         1.593750     1.853474      0.8609361
## covNtSerPolSay          -3.117003         1.734375     2.074018      0.7340425
## rareNoWorr              -3.811344         1.320312     1.623867      0.6365630
## flu                     -4.051239         1.726562     2.214502      1.2482165
## bgThrt                  -4.221150         1.984375     2.474320      1.1724998
## afrDie                  -7.361134         3.046875     3.950151      1.2677451
##                         Overall sd      p.value
## redIntWthChina            1.449906 5.242736e-17
## ctiusAsian                1.020504 9.916397e-12
## stpCovStpImmi             1.445240 1.884582e-11
## covNgeenLab               1.414998 1.329941e-07
## medOrgRecBstInt           1.280033 5.565647e-07
## medOrgUntrust             1.427163 3.217801e-06
## covNat                    1.403655 3.633578e-06
## chnsCovRcst               1.710810 6.139184e-06
## skeptInfoDocSci           1.209350 3.381785e-05
## covPlnnd                  1.160797 9.730260e-05
## frGovUseCovMndtVacc       1.429428 4.591874e-03
## polBgDlIntrst             1.797906 5.217119e-03
## scntFkNwsCov              1.359782 1.127605e-02
## mdiaUseCovMkTrmpRepLkBd   1.507580 2.234712e-02
## fllwRecMedOrgImp          1.123636 3.290593e-02
## eldrNoBgDl                1.045396 1.748069e-02
## polDwnplCovPlpLDngr       1.213461 7.056832e-03
## covNtSerPolSay            1.371579 1.826999e-03
## rareNoWorr                1.002522 1.382135e-04
## flu                       1.516047 5.094722e-05
## bgThrt                    1.461005 2.430590e-05
## afrDie                    1.544582 1.823549e-13
## 
## $`3`
##                            v.test Mean in category Overall mean sd in category
## mdiaCovBgrDl             8.966381         3.706294     2.649547      1.1577176
## afrDie                   8.561785         4.930070     3.950151      1.0944894
## polBgDlIntrst            8.122493         4.370629     3.288520      1.3045285
## bgThrt                   7.051951         3.237762     2.474320      1.5326655
## covNtSerPolSay           6.014732         2.685315     2.074018      1.1909894
## mdiaUseCovMkTrmpRepLkBd  4.933610         2.965035     2.413897      1.3401419
## flu                      4.751277         2.748252     2.214502      1.3764682
## rareNoWorr               4.498473         1.958042     1.623867      1.0830811
## polDwnplCovPlpLDngr      4.040501         2.216783     1.853474      1.2010477
## eldrNoBgDl               3.434274         1.979021     1.712991      1.2087590
## covNgeenLab             -2.278409         1.965035     2.203927      1.1968057
## covNat                  -2.584242         2.300699     2.569486      1.1468105
## skeptInfoDocSci         -3.012769         1.636364     1.906344      0.8239887
## thnksNoCovVacc          -3.070873         1.223776     1.451662      0.5344048
## covPlnnd                -3.544550         1.468531     1.773414      0.7174566
## redIntWthChina          -3.974181         1.748252     2.175227      1.0273772
##                         Overall sd      p.value
## mdiaCovBgrDl              1.590519 3.064164e-19
## afrDie                    1.544582 1.111335e-17
## polBgDlIntrst             1.797906 4.567026e-16
## bgThrt                    1.461005 1.764265e-12
## covNtSerPolSay            1.371579 1.801847e-09
## mdiaUseCovMkTrmpRepLkBd   1.507580 8.072373e-07
## flu                       1.516047 2.021360e-06
## rareNoWorr                1.002522 6.844331e-06
## polDwnplCovPlpLDngr       1.213461 5.333711e-05
## eldrNoBgDl                1.045396 5.941423e-04
## covNgeenLab               1.414998 2.270221e-02
## covNat                    1.403655 9.759336e-03
## skeptInfoDocSci           1.209350 2.588758e-03
## thnksNoCovVacc            1.001474 2.134339e-03
## covPlnnd                  1.160797 3.932841e-04
## redIntWthChina            1.449906 7.062187e-05
## 
## $`4`
##                            v.test Mean in category Overall mean sd in category
## covNtSerPolSay          16.116064         4.000000     2.074018       1.328020
## thnksNoCovVacc          16.077167         2.854545     1.451662       1.494453
## covPlnnd                15.992419         3.390909     1.773414       1.301017
## fllwRecMedOrgImp        15.751554         3.445455     1.903323       1.156691
## skeptInfoDocSci         15.728036         3.563636     1.906344       1.156226
## frGovUseCovMndtVacc     15.329330         3.954545     2.045317       1.479474
## covVacEffRedVirus       14.930891         3.527273     1.962236       1.405891
## scntFkNwsCov            14.733566         3.800000     2.054381       1.312873
## mdiaCovBgrDl            14.205414         4.618182     2.649547       1.198346
## covNgeenLab             13.535548         3.872727     2.203927       1.321907
## medOrgUntrust           13.493723         3.900000     2.222054       1.313912
## rareNoWorr              13.256351         2.781818     1.623867       1.238634
## mdiaUseCovMkTrmpRepLkBd 13.043666         4.127273     2.413897       1.251247
## polDwnplCovPlpLDngr     12.993456         3.227273     1.853474       1.392572
## medOrgRecBstInt         12.395510         3.600000     2.217523       1.207552
## covNat                  11.250597         3.945455     2.569486       1.263734
## eldrNoBgDl              10.636362         2.681818     1.712991       1.220520
## polBgDlIntrst            9.880700         4.836364     3.288520       1.074728
## chnsCovRcst              9.297939         3.763636     2.377644       1.747347
## redIntWthChina           9.263141         3.345455     2.175227       1.592233
## flu                      8.561686         3.345455     2.214502       1.404126
## bgThrt                   7.414536         3.418182     2.474320       1.448396
## stpCovStpImmi            7.214278         3.200000     2.291541       1.469694
## nwsGdJbComCov            6.483177         3.945455     3.235650       1.241966
## afrDie                   5.301536         4.663636     3.950151       1.390077
## ctiusAsian               5.095311         1.963636     1.510574       1.264388
##                         Overall sd      p.value
## covNtSerPolSay            1.371579 1.967484e-58
## thnksNoCovVacc            1.001474 3.688685e-58
## covPlnnd                  1.160797 1.443167e-57
## fllwRecMedOrgImp          1.123636 6.701450e-56
## skeptInfoDocSci           1.209350 9.717851e-56
## frGovUseCovMndtVacc       1.429428 4.869804e-53
## covVacEffRedVirus         1.203002 2.074740e-50
## scntFkNwsCov              1.359782 3.924419e-49
## mdiaCovBgrDl              1.590519 8.480047e-46
## covNgeenLab               1.414998 9.646033e-42
## medOrgUntrust             1.427163 1.702793e-41
## rareNoWorr                1.002522 4.145840e-40
## mdiaUseCovMkTrmpRepLkBd   1.507580 6.905503e-39
## polDwnplCovPlpLDngr       1.213461 1.332708e-38
## medOrgRecBstInt           1.280033 2.763830e-35
## covNat                    1.403655 2.300306e-29
## eldrNoBgDl                1.045396 2.018568e-26
## polBgDlIntrst             1.797906 5.047906e-23
## chnsCovRcst               1.710810 1.431948e-20
## redIntWthChina            1.449906 1.985045e-20
## flu                       1.516047 1.112287e-17
## bgThrt                    1.461005 1.220509e-13
## stpCovStpImmi             1.445240 5.422100e-13
## nwsGdJbComCov             1.256547 8.981131e-11
## afrDie                    1.544582 1.148326e-07
## ctiusAsian                1.020504 3.481697e-07
save(res.mfa, res.hcpc, file='./data/mfa_and_classification.rdata')

Analysis

df_covmis$cov_class <- res.hcpc$data.clust$clust
freq(df_covmis$cov_class)
##     n    % val%
## 1 281 42.4 42.4
## 2 128 19.3 19.3
## 3 143 21.6 21.6
## 4 110 16.6 16.6
df_covmis %>% 
  gather(key = "Covmis_var", value="Covmis_res", df_covmis %>% colnames %>% str_detect("^covmis") %>% which) %>%
  ggplot(., aes(y=Covmis_res, x= cov_class, fill=cov_class)) +
  geom_boxplot() +
  facet_wrap(~ Covmis_var, ncol = 5)

df_covmis %>% 
  gather(key = "Covmis_var", value="Covmis_res", df_covmis %>% colnames %>% str_detect("^covmis") %>% which) %>%
  mutate(Covmis_var=str_remove(Covmis_var,"^covmis_")) %>%
  ggplot(., aes(y=Covmis_res, x= Covmis_var, fill=Covmis_var)) +
  geom_boxplot() +
  facet_wrap(~ cov_class, ncol = 1)

table(df_covmis$EXPGRP_TEXT, df_covmis$cov_class) %>% cprop
##                    
##                     1     2     3     4     All  
##   Chinese            39.5  41.4  30.8  15.5  34.0
##   Non-Chinese Asian   1.4   3.1   2.1   3.6   2.3
##   White              59.1  55.5  67.1  80.9  63.7
##   Total             100.0 100.0 100.0 100.0 100.0
table(df_covmis$EXPGRP_TEXT, df_covmis$cov_class) %>% chisq.test()
## Warning in chisq.test(.): Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  .
## X-squared = 26.043, df = 6, p-value = 0.0002185
table(df_covmis$CONTINENT_BORN_TEXT_3, df_covmis$cov_class) %>% lprop
##                            
##                             1     2     3     4     Total
##   Northern Country (Richer)  30.5  26.2  27.7  15.6 100.0
##   Southern Country (Poorer)  32.6  17.4  28.5  21.5 100.0
##   USA                        50.8  17.5  16.7  15.1 100.0
##   All                        42.5  19.3  21.6  16.6 100.0
table(df_covmis$HAS_LIVED_USA, df_covmis$cov_class) %>% lprop
##        
##         1     2     3     4     Total
##   FALSE  28.4  21.8  29.6  20.2 100.0
##   TRUE   50.6  17.9  16.9  14.6 100.0
##   All    42.4  19.3  21.6  16.6 100.0

By considering the data has quatitative and not qualitative we tend to put closer the outsider that might have answered with extreme response in misconsception data and those who answered in the middle. Let’s try to see if we get different result when analysing qualitative data.

MFA and HCPC with qualitative data

res_mfa_quali <- df_covmis %>%
  dplyr::select(matches("^covmis_")) %>%
  transmute_all(~fct_recode(.x %>% as_factor,
                                   "-"="1",
                                   "-"="2",
                                   "="="3",
                                   "="="4",
                                   "+"="5",
                                   "+"="6")) %>% 
  rename_with(function(x){str_remove(x,"^covmis_[A-z]{3,4}_")}) %>%
  MFA(.,
      group = c(5,4,4,3,3,3,4),
      type=c("n","n","n","n","n","n","n"),
      name.group = c("att","conspiracy","origin","politics","coverage","antivaccin","Mdsk"))
## Warning: ggrepel: 75 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 650 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning: ggrepel: 631 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

fviz_screeplot(res_mfa_quali)

fviz_mfa_var(res_mfa_quali, "quali.var", palette = "jco", 
             col.var.sup = "violet", repel = TRUE)
## Warning: ggrepel: 46 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

quali.var <- get_mfa_var(res_mfa_quali, "quali.var")
quali.var$coord
##                                 Dim.1        Dim.2        Dim.3        Dim.4
## flu_-                     -0.63190250  0.050833492  0.220603615 -0.102234292
## flu_=                      1.91188535 -0.686324554 -0.653615784  0.318664496
## flu_+                      0.77387816  0.811082397 -0.292871148  0.109879710
## afrDie_-                  -0.70772318  0.362624553  0.597935863  0.144596989
## afrDie_=                  -0.36586938 -0.269474945  0.275542370 -0.224066614
## afrDie_+                   0.65293633  0.099375680 -0.519767947  0.150366717
## eldrNoBgDl_-              -0.47282014  0.032429636  0.108111291 -0.054574598
## eldrNoBgDl_=               2.14581844 -0.375682940 -0.434485524 -0.044356928
## eldrNoBgDl_+               2.36846186  1.107031792 -0.853555003  1.895793010
## rareNoWorr_-              -0.52996521  0.043329181  0.147796910 -0.080355204
## rareNoWorr_=               2.64341551 -0.423066246 -0.741149043  0.268203975
## rareNoWorr_+               3.44950345  1.092676099 -0.935744589  1.403869178
## bgThrt_-                  -0.65636413  0.079367932  0.316227105 -0.022161560
## bgThrt_=                   0.72044850 -0.465718497 -0.466751101  0.083667421
## bgThrt_+                   1.50511906  0.717178561 -0.440978948 -0.090118483
## ctiusAsian_-              -0.21275763  0.083061295 -0.153507652 -0.043151998
## ctiusAsian_=               1.34381146 -0.783297671  0.661139793 -0.169574038
## ctiusAsian_+               1.49787178  0.691327033  2.602369312  2.484971570
## stpCovStpImmi_-           -0.54576531  0.122238483 -0.257615393 -0.041449448
## stpCovStpImmi_=            0.74868432 -0.556876503  0.093539163 -0.231879835
## stpCovStpImmi_+            1.51407766  0.981345121  1.596349329  1.094645337
## redIntWthChina_-          -0.60736923  0.104287438 -0.305395915  0.007417633
## redIntWthChina_=           0.93359456 -0.665544000  0.245126346 -0.438047733
## redIntWthChina_+           2.10827386  1.144543293  1.728903079  1.246433032
## chnsCovRcst_-             -0.72490561  0.109846442 -0.161088716 -0.067198938
## chnsCovRcst_=              0.97892904 -0.911042459  0.096985230 -0.212512708
## chnsCovRcst_+              1.76094828  0.646964797  0.535754103  0.526579793
## covPlnnd_-                -0.63762873 -0.016471134 -0.142553390  0.104135949
## covPlnnd_=                 2.15719286 -0.471287584  0.478610611 -0.289407343
## covPlnnd_+                 3.94956516  2.420876866  0.899138777 -0.921793491
## covNat_-                  -0.96434563  0.108142267 -0.188720037  0.137253401
## covNat_=                   0.86059857 -0.526803498  0.078175080 -0.046085090
## covNat_+                   1.93256999  1.030034090  0.639669297 -0.496429326
## covNgeenLab_-             -0.86166156  0.070903889 -0.154662695  0.118085697
## covNgeenLab_=              1.21152106 -0.711187678  0.132702439 -0.060550725
## covNgeenLab_+              2.88978652  1.607419243  0.774458373 -0.714322489
## scntFkNwsCov_-            -0.87380568  0.097393958 -0.093478240  0.074819444
## scntFkNwsCov_=             1.64927766 -0.853163231  0.293735550 -0.058287603
## scntFkNwsCov_+             3.10706999  2.251346850 -0.122841408 -0.587895395
## polBgDlIntrst_-           -1.44709232  0.289405556  0.090855062 -0.029891094
## polBgDlIntrst_=            0.55758661 -0.803900183  0.015890057 -0.158183705
## polBgDlIntrst_+            1.30928392  0.536782132 -0.140910440  0.222784533
## covNtSerPolSay_-          -0.89138541  0.122403585  0.176116056 -0.067608424
## covNtSerPolSay_=           1.79141834 -1.083564852 -0.247786612  0.050942791
## covNtSerPolSay_+           3.71343664  2.057411763 -1.059069877  0.541979733
## polDwnplCovPlpLDngr_-     -0.69839089  0.067477712  0.162500932 -0.061615364
## polDwnplCovPlpLDngr_=      2.14943859 -1.001469181 -0.416200359  0.082822329
## polDwnplCovPlpLDngr_+      3.01604711  2.463521473 -0.993053909  0.636788254
## mdiaCovBgrDl_-            -1.23885054  0.259024994  0.167471971 -0.079685559
## mdiaCovBgrDl_=             0.91602909 -1.001106249  0.017913283 -0.123011516
## mdiaCovBgrDl_+             2.63056351  1.239583047 -0.668939821  0.571370135
## nwsGdJbComCov_-           -0.72493714  0.151374120  0.284983197  0.097067952
## nwsGdJbComCov_=            0.08717035 -0.348368210 -0.218571211 -0.051326630
## nwsGdJbComCov_+            0.89886069  0.878774855  0.241960919  0.007732009
## mdiaUseCovMkTrmpRepLkBd_- -1.12248341  0.159968135  0.049544190 -0.091085464
## mdiaUseCovMkTrmpRepLkBd_=  1.22914967 -0.735295879 -0.175622148 -0.037433153
## mdiaUseCovMkTrmpRepLkBd_+  2.45419741  1.291179539  0.247239184  0.601011671
## frGovUseCovMndtVacc_-     -0.84590295  0.094174426 -0.079025286  0.064571950
## frGovUseCovMndtVacc_=      1.70326864 -1.066921952  0.305658054  0.087002171
## frGovUseCovMndtVacc_+      3.11500872  1.556494194 -0.026902032 -0.708609392
## thnksNoCovVacc_-          -0.46380830  0.004210011 -0.008641680  0.090278369
## thnksNoCovVacc_=           3.06075464 -0.943742616  0.214828999 -0.199166483
## thnksNoCovVacc_+           4.18743783  3.740325403 -0.572908736 -2.451029121
## covVacEffRedVirus_-       -0.74228722  0.077724378 -0.004548361  0.093934800
## covVacEffRedVirus_=        1.69244885 -0.857500513  0.095435578  0.023925275
## covVacEffRedVirus_+        3.87793482  2.732034741 -0.368635083 -1.589080538
## medOrgUntrust_-           -0.89597500  0.110754939 -0.176203620 -0.005278715
## medOrgUntrust_=            1.45046553 -0.856373659  0.291036767  0.174068186
## medOrgUntrust_+            2.51175409  1.708861818  0.476707442 -0.478865800
## skeptInfoDocSci_-         -0.75334424  0.032219501 -0.144879549  0.048115568
## skeptInfoDocSci_=          2.05362277 -0.682955092  0.530624780  0.039510823
## skeptInfoDocSci_+          3.74536243  2.952774686  0.010569411 -1.131971388
## medOrgRecBstInt_-         -0.93985508  0.183772105 -0.181016183  0.054420631
## medOrgRecBstInt_=          1.46891595 -0.744657684  0.309630419  0.028337992
## medOrgRecBstInt_+          2.57089140  1.784492712  0.361568640 -0.715827621
## fllwRecMedOrgImp_-        -0.79417647  0.071625960 -0.081299337  0.063930777
## fllwRecMedOrgImp_=         2.02121478 -0.653157916  0.231651854 -0.050889419
## fllwRecMedOrgImp_+         3.95532158  3.245403330  0.215632118 -1.173803452
##                                   Dim.5
## flu_-                     -0.0079655558
## flu_=                      0.0461187789
## flu_+                     -0.0263545589
## afrDie_-                   0.1221272809
## afrDie_=                   0.2084621420
## afrDie_+                  -0.2506808604
## eldrNoBgDl_-               0.0134360912
## eldrNoBgDl_=              -0.1512401298
## eldrNoBgDl_+               0.4341544094
## rareNoWorr_-              -0.0466447617
## rareNoWorr_=               0.2228263942
## rareNoWorr_+               0.3689277201
## bgThrt_-                  -0.0005361463
## bgThrt_=                  -0.0182044801
## bgThrt_+                   0.0458627573
## ctiusAsian_-              -0.0658698155
## ctiusAsian_=               0.7766442667
## ctiusAsian_+              -1.3152180963
## stpCovStpImmi_-           -0.1044003574
## stpCovStpImmi_=            0.3415882043
## stpCovStpImmi_+           -0.3834144668
## redIntWthChina_-          -0.1087472639
## redIntWthChina_=           0.3672394473
## redIntWthChina_+          -0.2191314814
## chnsCovRcst_-             -0.0037260705
## chnsCovRcst_=              0.2755342662
## chnsCovRcst_+             -0.3150412035
## covPlnnd_-                -0.0366525791
## covPlnnd_=                 0.4177622784
## covPlnnd_+                -1.0655176572
## covNat_-                  -0.0834695759
## covNat_=                   0.2167123599
## covNat_+                  -0.2448062201
## covNgeenLab_-             -0.0171709775
## covNgeenLab_=              0.2444473914
## covNgeenLab_+             -0.6071915454
## scntFkNwsCov_-             0.0050214802
## scntFkNwsCov_=             0.0963669372
## scntFkNwsCov_+            -0.4286339810
## polBgDlIntrst_-            0.1908127988
## polBgDlIntrst_=           -0.1403244275
## polBgDlIntrst_+           -0.0955901508
## covNtSerPolSay_-           0.1099522485
## covNtSerPolSay_=          -0.6609065293
## covNtSerPolSay_+           0.8904457090
## polDwnplCovPlpLDngr_-      0.0079294445
## polDwnplCovPlpLDngr_=     -0.4060573030
## polDwnplCovPlpLDngr_+      1.2903160314
## mdiaCovBgrDl_-             0.2139165072
## mdiaCovBgrDl_=            -0.5275562955
## mdiaCovBgrDl_+             0.3623013579
## nwsGdJbComCov_-           -0.4680839584
## nwsGdJbComCov_=            0.0980314926
## nwsGdJbComCov_+            0.4455394121
## mdiaUseCovMkTrmpRepLkBd_-  0.1631389586
## mdiaUseCovMkTrmpRepLkBd_= -0.2850575237
## mdiaUseCovMkTrmpRepLkBd_+ -0.0449331861
## frGovUseCovMndtVacc_-      0.0313865589
## frGovUseCovMndtVacc_=     -0.0055281030
## frGovUseCovMndtVacc_+     -0.2406952256
## thnksNoCovVacc_-          -0.0130021570
## thnksNoCovVacc_=           0.2337653673
## thnksNoCovVacc_+          -0.4929539504
## covVacEffRedVirus_-       -0.0299962709
## covVacEffRedVirus_=        0.1917538015
## covVacEffRedVirus_+       -0.4123423681
## medOrgUntrust_-           -0.0788470553
## medOrgUntrust_=            0.2772670746
## medOrgUntrust_+           -0.2252093623
## skeptInfoDocSci_-         -0.0366734303
## skeptInfoDocSci_=          0.2132988371
## skeptInfoDocSci_+         -0.4104587195
## medOrgRecBstInt_-         -0.1184202885
## medOrgRecBstInt_=          0.1906834328
## medOrgRecBstInt_+          0.2959168797
## fllwRecMedOrgImp_-        -0.1048376559
## fllwRecMedOrgImp_=         0.3899442294
## fllwRecMedOrgImp_+        -0.4197926697
quali.var$contrib
##                                Dim.1        Dim.2        Dim.3        Dim.4
## flu_-                     0.77386479 0.0213882301 1.354803e+00 0.4541934509
## flu_=                     1.87791426 1.0335254067 3.152703e+00 1.1697745645
## flu_+                     0.18760888 0.8801327619 3.859645e-01 0.0848058823
## afrDie_-                  0.25104701 0.2814832977 2.574088e+00 0.2349792164
## afrDie_=                  0.14760566 0.3419776181 1.202579e+00 1.2413319488
## afrDie_+                  0.49503216 0.0489735965 4.506057e+00 0.5886781766
## eldrNoBgDl_-              0.50796969 0.0102056344 3.814818e-01 0.1517433601
## eldrNoBgDl_=              1.92323744 0.2517676462 1.132618e+00 0.0184269145
## eldrNoBgDl_+              0.42174725 0.3935044645 7.868073e-01 6.0587641071
## rareNoWorr_-              0.65108046 0.0185870693 7.273720e-01 0.3356222167
## rareNoWorr_=              2.71431830 0.2969317757 3.064975e+00 0.6265317333
## rareNoWorr_+              0.69580599 0.2981727115 7.354879e-01 2.5841101225
## bgThrt_-                  0.70537929 0.0440487079 2.351891e+00 0.0180308587
## bgThrt_=                  0.41191389 0.7351188295 2.483460e+00 0.1245650112
## bgThrt_+                  0.75696998 0.7340092885 9.333802e-01 0.0608481585
## ctiusAsian_-              0.11738204 0.0764079877 8.777631e-01 0.1082719376
## ctiusAsian_=              0.60476288 0.8775505294 2.102718e+00 0.2159284746
## ctiusAsian_+              0.15230609 0.1385622988 6.603767e+00 9.3992582222
## stpCovStpImmi_-           0.56076631 0.1201423085 1.794732e+00 0.0725254285
## stpCovStpImmi_=           0.48197865 1.1388279823 1.080695e-01 1.0366664364
## stpCovStpImmi_+           0.58097983 1.0423613615 9.276978e+00 6.8091729155
## redIntWthChina_-          0.73958148 0.0931224648 2.685918e+00 0.0024733973
## redIntWthChina_=          0.64690007 1.4040550476 6.405993e-01 3.1933476020
## redIntWthChina_+          1.10635236 1.3925598858 1.068727e+01 8.6708178338
## chnsCovRcst_-             1.02022729 0.1000497544 7.236860e-01 0.1965810174
## chnsCovRcst_=             0.55078597 2.0373596398 7.765646e-02 0.5820134271
## chnsCovRcst_+             1.48756446 0.8575390541 1.977878e+00 2.9825911005
## covPlnnd_-                0.76480252 0.0021795686 5.491039e-01 0.4574015686
## covPlnnd_=                1.82714732 0.3724582757 1.291951e+00 0.7373903596
## covPlnnd_+                1.39200728 2.2335637684 1.036292e+00 1.7001723963
## covNat_-                  1.18836922 0.0638244251 6.537443e-01 0.5397776975
## covNat_=                  0.59746520 0.9561338333 7.081642e-02 0.0384162522
## covNat_+                  1.03984354 1.2615682377 1.636419e+00 1.5384916311
## covNgeenLab_-             1.14753057 0.0331849336 5.310653e-01 0.4832458863
## covNgeenLab_=             0.90114217 1.3262024961 1.553013e-01 0.0504722681
## covNgeenLab_+             1.69906233 2.2451539647 1.752911e+00 2.3278188488
## scntFkNwsCov_-            1.24551472 0.0660835220 2.047510e-01 0.2047532626
## scntFkNwsCov_=            1.58262525 1.8086928089 7.210900e-01 0.0443226455
## scntFkNwsCov_+            1.44728578 3.2452417561 3.249584e-02 1.1618099330
## polBgDlIntrst_-           2.69343582 0.4600848105 1.525100e-01 0.0257679885
## polBgDlIntrst_=           0.34186539 3.0349024332 3.988111e-03 0.6169311461
## polBgDlIntrst_+           1.63419338 1.1731185224 2.718993e-01 1.0609345466
## covNtSerPolSay_-          1.90370051 0.1533083721 1.067459e+00 0.2455567904
## covNtSerPolSay_=          2.28237559 3.5662592907 6.272413e-01 0.0413848425
## covNtSerPolSay_+          3.19951270 4.1945391659 3.738238e+00 1.5282015688
## polDwnplCovPlpLDngr_-     1.25470400 0.0500235906 9.757572e-01 0.2189802387
## polDwnplCovPlpLDngr_=     2.74983137 2.5494187194 1.480972e+00 0.0915447836
## polDwnplCovPlpLDngr_+     1.56001628 4.4450393160 2.429318e+00 1.5592857912
## mdiaCovBgrDl_-            3.20209385 0.5978463550 8.405555e-01 0.2970559257
## mdiaCovBgrDl_=            1.02983071 5.2531241524 5.656966e-03 0.4164104221
## mdiaCovBgrDl_+            3.84192781 3.6434506161 3.568713e+00 4.0641472569
## nwsGdJbComCov_-           0.56512762 0.1052347355 1.254499e+00 0.2271853433
## nwsGdJbComCov_=           0.01620908 1.1056251911 1.463837e+00 0.1260053918
## nwsGdJbComCov_+           0.53356978 2.1780693964 5.553705e-01 0.0008852662
## mdiaUseCovMkTrmpRepLkBd_- 2.82024379 0.2446266466 7.892201e-02 0.4163970892
## mdiaUseCovMkTrmpRepLkBd_= 1.83654208 2.8068914986 5.385621e-01 0.0381932756
## mdiaUseCovMkTrmpRepLkBd_+ 2.49922496 2.9544023846 3.643400e-01 3.3607400174
## frGovUseCovMndtVacc_-     1.48465769 0.0785889045 1.861244e-01 0.1939794857
## frGovUseCovMndtVacc_=     1.62206374 2.7181719815 7.503419e-01 0.0948952649
## frGovUseCovMndtVacc_+     2.50070344 2.6665435544 2.679167e-03 2.9016178310
## thnksNoCovVacc_-          0.54500020 0.0001917768 2.717702e-03 0.4629880591
## thnksNoCovVacc_=          2.70080118 1.0966113304 1.911205e-01 0.2564193747
## thnksNoCovVacc_+          1.22548606 4.1758069845 3.295099e-01 9.4143711559
## covVacEffRedVirus_-       1.17450653 0.0549966189 6.334425e-04 0.4217423791
## covVacEffRedVirus_=       1.78919957 1.9615820306 8.172110e-02 0.0080172290
## covVacEffRedVirus_+       2.03635902 4.3165401903 2.643216e-01 7.6670430626
## medOrgUntrust_-           1.17977515 0.0769916599 6.554250e-01 0.0009182198
## medOrgUntrust_=           1.20831998 1.7988864244 6.987933e-01 0.3902012715
## medOrgUntrust_+           1.21491893 2.4016901108 6.286122e-01 0.9901556060
## skeptInfoDocSci_-         0.95868328 0.0074892073 5.093171e-01 0.0876884107
## skeptInfoDocSci_=         1.93775317 0.9152746300 1.858310e+00 0.0160831831
## skeptInfoDocSci_+         1.23219447 3.2708617478 1.409545e-04 2.5237434230
## medOrgRecBstInt_-         1.25936740 0.2056363337 6.710446e-01 0.0946761423
## medOrgRecBstInt_=         1.45794820 1.6001900576 9.305107e-01 0.0121665965
## medOrgRecBstInt_+         0.89319372 1.8378826172 2.537731e-01 1.5526653042
## fllwRecMedOrgImp_-        1.04198411 0.0361974516 1.568509e-01 0.1514013622
## fllwRecMedOrgImp_=        2.11171142 0.9417942663 3.984438e-01 0.0300156235
## fllwRecMedOrgImp_+        1.05708960 3.0394539810 4.512955e-02 2.0874770661
##                                  Dim.5
## flu_-                     3.333352e-03
## flu_=                     2.962045e-02
## flu_+                     5.897986e-03
## afrDie_-                  2.026458e-01
## afrDie_=                  1.298941e+00
## afrDie_+                  1.977963e+00
## eldrNoBgDl_-              1.111926e-02
## eldrNoBgDl_=              2.589793e-01
## eldrNoBgDl_+              3.841421e-01
## rareNoWorr_-              1.367193e-01
## rareNoWorr_=              5.228140e-01
## rareNoWorr_+              2.157454e-01
## bgThrt_-                  1.275803e-05
## bgThrt_=                  7.129206e-03
## bgThrt_+                  1.905203e-02
## ctiusAsian_-              3.049919e-01
## ctiusAsian_=              5.475664e+00
## ctiusAsian_+              3.183080e+00
## stpCovStpImmi_-           5.562347e-01
## stpCovStpImmi_=           2.719694e+00
## stpCovStpImmi_+           1.009917e+00
## redIntWthChina_-          6.426895e-01
## redIntWthChina_=          2.713334e+00
## redIntWthChina_+          3.239911e-01
## chnsCovRcst_-             7.306682e-04
## chnsCovRcst_=             1.182812e+00
## chnsCovRcst_+             1.290629e+00
## covPlnnd_-                6.850254e-02
## covPlnnd_=                1.857540e+00
## covPlnnd_+                2.746302e+00
## covNat_-                  2.413384e-01
## covNat_=                  1.026982e+00
## covNat_+                  4.522999e-01
## covNgeenLab_-             1.235279e-02
## covNgeenLab_=             9.944575e-01
## covNgeenLab_+             2.033355e+00
## scntFkNwsCov_-            1.114979e-03
## scntFkNwsCov_=            1.464639e-01
## scntFkNwsCov_+            7.466369e-01
## polBgDlIntrst_-           1.269443e+00
## polBgDlIntrst_=           5.869229e-01
## polBgDlIntrst_+           2.361273e-01
## covNtSerPolSay_-          7.851631e-01
## covNtSerPolSay_=          8.420875e+00
## covNtSerPolSay_+          4.986901e+00
## polDwnplCovPlpLDngr_-     4.384436e-03
## polDwnplCovPlpLDngr_=     2.660202e+00
## polDwnplCovPlpLDngr_+     7.739789e+00
## mdiaCovBgrDl_-            2.588027e+00
## mdiaCovBgrDl_=            9.259104e+00
## mdiaCovBgrDl_+            1.975493e+00
## nwsGdJbComCov_-           6.386713e+00
## nwsGdJbComCov_=           5.556946e-01
## nwsGdJbComCov_+           3.553553e+00
## mdiaUseCovMkTrmpRepLkBd_- 1.614828e+00
## mdiaUseCovMkTrmpRepLkBd_= 2.677567e+00
## mdiaUseCovMkTrmpRepLkBd_+ 2.270936e-02
## frGovUseCovMndtVacc_-     5.540599e-02
## frGovUseCovMndtVacc_=     4.631673e-04
## frGovUseCovMndtVacc_+     4.047274e-01
## thnksNoCovVacc_-          1.161006e-02
## thnksNoCovVacc_=          4.270512e-01
## thnksNoCovVacc_+          4.603714e-01
## covVacEffRedVirus_-       5.199123e-02
## covVacEffRedVirus_=       6.225865e-01
## covVacEffRedVirus_+       6.240987e-01
## medOrgUntrust_-           2.476638e-01
## medOrgUntrust_=           1.196872e+00
## medOrgUntrust_+           2.647586e-01
## skeptInfoDocSci_-         6.158505e-02
## skeptInfoDocSci_=         5.666542e-01
## skeptInfoDocSci_+         4.011572e-01
## medOrgRecBstInt_-         5.419596e-01
## medOrgRecBstInt_=         6.659754e-01
## medOrgRecBstInt_+         3.207759e-01
## fllwRecMedOrgImp_-        4.922047e-01
## fllwRecMedOrgImp_=        2.130584e+00
## fllwRecMedOrgImp_+        3.227765e-01

Cassification

res.hcpc <- HCPC(res_mfa_quali, nb.clust=3, graph = T)

fviz_cluster(res.hcpc,
             repel = TRUE, 
             show.clust.cent = TRUE, 
             palette = "jco", 
             ggtheme = theme_minimal(),
             main = "Factor map"
             )
## Warning: ggrepel: 597 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

res.hcpc$desc.var$category
## $`1`
##                                                     Cla/Mod    Mod/Cla
## covNtSerPolSay=covNtSerPolSay_-                   88.000000 93.0957684
## mdiaCovBgrDl=mdiaCovBgrDl_-                       94.117647 74.8329621
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 92.167102 78.6191537
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_-         85.684211 90.6458797
## scntFkNwsCov=scntFkNwsCov_-                       86.433260 87.9732739
## skeptInfoDocSci=skeptInfoDocSci_-                 83.400000 92.8730512
## fllwRecMedOrgImp=fllwRecMedOrgImp_-               83.844581 91.3140312
## medOrgRecBstInt=medOrgRecBstInt_-                 87.440758 82.1826281
## covVacEffRedVirus=covVacEffRedVirus_-             83.606557 90.8685969
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_-         82.156863 93.3184855
## medOrgUntrust=medOrgUntrust_-                     86.206897 83.5189310
## polBgDlIntrst=polBgDlIntrst_-                     96.078431 54.5657016
## covNgeenLab=covNgeenLab_-                         85.681293 82.6280624
## covPlnnd=covPlnnd_-                               80.265655 94.2093541
## thnksNoCovVacc=thnksNoCovVacc_-                   76.724138 99.1091314
## rareNoWorr=rareNoWorr_-                           78.198198 96.6592428
## covNat=covNat_-                                   86.592179 69.0423163
## eldrNoBgDl=eldrNoBgDl_-                           77.573529 93.9866370
## chnsCovRcst=chnsCovRcst_-                         82.284382 78.6191537
## flu=flu_-                                         80.387931 83.0734967
## redIntWthChina=redIntWthChina_-                   78.329571 77.2828508
## stpCovStpImmi=stpCovStpImmi_-                     79.086538 73.2739421
## bgThrt=bgThrt_-                                   79.591837 69.4877506
## ctiusAsian=ctiusAsian_-                           72.076789 91.9821826
## afrDie=afrDie_-                                   82.500000 22.0489978
## nwsGdJbComCov=nwsGdJbComCov_-                     78.804348 32.2939866
## afrDie=afrDie_=                                   75.378788 44.3207127
## nwsGdJbComCov=nwsGdJbComCov_=                     64.383562 52.3385301
## eldrNoBgDl=eldrNoBgDl_+                           27.777778  1.1135857
## stpCovStpImmi=stpCovStpImmi_+                     44.642857  5.5679287
## medOrgRecBstInt=medOrgRecBstInt_+                 37.500000  3.3407572
## bgThrt=bgThrt_+                                   45.000000  8.0178174
## rareNoWorr=rareNoWorr_+                            7.142857  0.2227171
## bgThrt=bgThrt_=                                   53.157895 22.4944321
## thnksNoCovVacc=thnksNoCovVacc_+                    6.250000  0.2227171
## skeptInfoDocSci=skeptInfoDocSci_+                 19.230769  1.1135857
## redIntWthChina=redIntWthChina_+                   34.545455  4.2316258
## redIntWthChina=redIntWthChina_=                   50.609756 18.4855234
## ctiusAsian=ctiusAsian_=                           39.189189  6.4587973
## fllwRecMedOrgImp=fllwRecMedOrgImp_+               10.000000  0.4454343
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+         23.529412  1.7817372
## medOrgUntrust=medOrgUntrust_+                     33.333333  4.2316258
## polBgDlIntrst=polBgDlIntrst_=                     52.752294 25.6124722
## chnsCovRcst=chnsCovRcst_=                         44.881890 12.6948775
## stpCovStpImmi=stpCovStpImmi_=                     50.000000 21.1581292
## covNat=covNat_+                                   35.897436  6.2360802
## covPlnnd=covPlnnd_+                                8.000000  0.4454343
## afrDie=afrDie_+                                   54.316547 33.6302895
## covVacEffRedVirus=covVacEffRedVirus_+             12.903226  0.8908686
## scntFkNwsCov=scntFkNwsCov_+                       16.666667  1.5590200
## polBgDlIntrst=polBgDlIntrst_+                     47.089947 19.8218263
## chnsCovRcst=chnsCovRcst_+                         36.792453  8.6859688
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+ 28.169014  4.4543430
## covNat=covNat_=                                   49.115044 24.7216036
## covNgeenLab=covNgeenLab_+                         17.543860  2.2271715
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+         15.254237  2.0044543
## covNgeenLab=covNgeenLab_=                         39.534884 15.1447661
## mdiaCovBgrDl=mdiaCovBgrDl_=                       42.857143 20.0445434
## covNtSerPolSay=covNtSerPolSay_+                    4.347826  0.4454343
## mdiaCovBgrDl=mdiaCovBgrDl_+                       24.210526  5.1224944
## eldrNoBgDl=eldrNoBgDl_=                           22.000000  4.8997773
## flu=flu_=                                         25.203252  6.9042316
## covPlnnd=covPlnnd_=                               21.818182  5.3452116
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_=         25.781250  7.3496659
## medOrgUntrust=medOrgUntrust_=                     32.352941 12.2494432
## rareNoWorr=rareNoWorr_=                           15.053763  3.1180401
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_= 36.538462 16.9265033
## thnksNoCovVacc=thnksNoCovVacc_=                    4.545455  0.6681514
## covVacEffRedVirus=covVacEffRedVirus_=             25.874126  8.2405345
## scntFkNwsCov=scntFkNwsCov_=                       28.834356 10.4677060
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_=         18.644068  4.8997773
## medOrgRecBstInt=medOrgRecBstInt_=                 32.500000 14.4766147
## fllwRecMedOrgImp=fllwRecMedOrgImp_=               24.183007  8.2405345
## skeptInfoDocSci=skeptInfoDocSci_=                 19.852941  6.0133630
## covNtSerPolSay=covNtSerPolSay_=                   20.567376  6.4587973
##                                                      Global      p.value
## covNtSerPolSay=covNtSerPolSay_-                   71.752266 5.117451e-70
## mdiaCovBgrDl=mdiaCovBgrDl_-                       53.927492 3.448964e-60
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 57.854985 1.502251e-58
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_-         71.752266 2.824687e-54
## scntFkNwsCov=scntFkNwsCov_-                       69.033233 3.902610e-52
## skeptInfoDocSci=skeptInfoDocSci_-                 75.528701 1.150138e-49
## fllwRecMedOrgImp=fllwRecMedOrgImp_-               73.867069 3.083620e-48
## medOrgRecBstInt=medOrgRecBstInt_-                 63.746224 1.115046e-46
## covVacEffRedVirus=covVacEffRedVirus_-             73.716012 1.895507e-46
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_-         77.039275 1.458702e-45
## medOrgUntrust=medOrgUntrust_-                     65.709970 2.917191e-44
## polBgDlIntrst=polBgDlIntrst_-                     38.519637 2.840440e-41
## covNgeenLab=covNgeenLab_-                         65.407855 2.870468e-41
## covPlnnd=covPlnnd_-                               79.607251 8.129743e-40
## thnksNoCovVacc=thnksNoCovVacc_-                   87.613293 2.492696e-38
## rareNoWorr=rareNoWorr_-                           83.836858 6.288206e-37
## covNat=covNat_-                                   54.078550 5.464027e-30
## eldrNoBgDl=eldrNoBgDl_-                           82.175227 6.475358e-29
## chnsCovRcst=chnsCovRcst_-                         64.803625 1.034163e-26
## flu=flu_-                                         70.090634 2.786292e-25
## redIntWthChina=redIntWthChina_-                   66.918429 5.561513e-16
## stpCovStpImmi=stpCovStpImmi_-                     62.839879 1.412665e-15
## bgThrt=bgThrt_-                                   59.214502 7.921409e-15
## ctiusAsian=ctiusAsian_-                           86.555891 1.151745e-08
## afrDie=afrDie_-                                   18.126888 8.326943e-05
## nwsGdJbComCov=nwsGdJbComCov_-                     27.794562 1.330351e-04
## afrDie=afrDie_=                                   39.879154 6.586012e-04
## nwsGdJbComCov=nwsGdJbComCov_=                     55.135952 3.583937e-02
## eldrNoBgDl=eldrNoBgDl_+                            2.719033 5.640024e-04
## stpCovStpImmi=stpCovStpImmi_+                      8.459215 2.006869e-04
## medOrgRecBstInt=medOrgRecBstInt_+                  6.042296 5.755603e-05
## bgThrt=bgThrt_+                                   12.084592 7.360221e-06
## rareNoWorr=rareNoWorr_+                            2.114804 3.161954e-06
## bgThrt=bgThrt_=                                   28.700906 4.944141e-07
## thnksNoCovVacc=thnksNoCovVacc_+                    2.416918 3.403001e-07
## skeptInfoDocSci=skeptInfoDocSci_+                  3.927492 3.019986e-07
## redIntWthChina=redIntWthChina_+                    8.308157 1.475632e-07
## redIntWthChina=redIntWthChina_=                   24.773414 1.082836e-07
## ctiusAsian=ctiusAsian_=                           11.178248 8.449483e-08
## fllwRecMedOrgImp=fllwRecMedOrgImp_+                3.021148 8.314454e-08
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+          5.135952 7.397510e-08
## medOrgUntrust=medOrgUntrust_+                      8.610272 2.764520e-08
## polBgDlIntrst=polBgDlIntrst_=                     32.930514 1.019481e-08
## chnsCovRcst=chnsCovRcst_=                         19.184290 2.529324e-09
## stpCovStpImmi=stpCovStpImmi_=                     28.700906 1.054052e-09
## covNat=covNat_+                                   11.782477 7.705449e-10
## covPlnnd=covPlnnd_+                                3.776435 3.208593e-10
## afrDie=afrDie_+                                   41.993958 3.033776e-10
## covVacEffRedVirus=covVacEffRedVirus_+              4.682779 1.361676e-10
## scntFkNwsCov=scntFkNwsCov_+                        6.344411 2.254740e-12
## polBgDlIntrst=polBgDlIntrst_+                     28.549849 1.632458e-12
## chnsCovRcst=chnsCovRcst_+                         16.012085 7.062492e-13
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+ 10.725076 4.578863e-13
## covNat=covNat_=                                   34.138973 2.646177e-13
## covNgeenLab=covNgeenLab_+                          8.610272 3.311659e-16
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+          8.912387 2.621952e-18
## covNgeenLab=covNgeenLab_=                         25.981873 2.390130e-19
## mdiaCovBgrDl=mdiaCovBgrDl_=                       31.722054 3.240580e-20
## covNtSerPolSay=covNtSerPolSay_+                    6.948640 4.383206e-21
## mdiaCovBgrDl=mdiaCovBgrDl_+                       14.350453 3.024052e-21
## eldrNoBgDl=eldrNoBgDl_=                           15.105740 9.441374e-25
## flu=flu_=                                         18.580060 2.017154e-27
## covPlnnd=covPlnnd_=                               16.616314 7.537646e-28
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_=         19.335347 4.935292e-28
## medOrgUntrust=medOrgUntrust_=                     25.679758 3.999760e-29
## rareNoWorr=rareNoWorr_=                           14.048338 3.730395e-30
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_= 31.419940 1.577483e-30
## thnksNoCovVacc=thnksNoCovVacc_=                    9.969789 1.425645e-30
## covVacEffRedVirus=covVacEffRedVirus_=             21.601208 4.638434e-32
## scntFkNwsCov=scntFkNwsCov_=                       24.622356 5.102054e-33
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_=         17.824773 1.466528e-34
## medOrgRecBstInt=medOrgRecBstInt_=                 30.211480 2.004316e-36
## fllwRecMedOrgImp=fllwRecMedOrgImp_=               23.111782 7.762233e-38
## skeptInfoDocSci=skeptInfoDocSci_=                 20.543807 3.302467e-39
## covNtSerPolSay=covNtSerPolSay_=                   21.299094 7.599613e-40
##                                                       v.test
## covNtSerPolSay=covNtSerPolSay_-                    17.688777
## mdiaCovBgrDl=mdiaCovBgrDl_-                        16.364134
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_-  16.132732
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_-          15.513195
## scntFkNwsCov=scntFkNwsCov_-                        15.193540
## skeptInfoDocSci=skeptInfoDocSci_-                  14.816259
## fllwRecMedOrgImp=fllwRecMedOrgImp_-                14.593626
## medOrgRecBstInt=medOrgRecBstInt_-                  14.346839
## covVacEffRedVirus=covVacEffRedVirus_-              14.309986
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_-          14.167366
## medOrgUntrust=medOrgUntrust_-                      13.955386
## polBgDlIntrst=polBgDlIntrst_-                      13.455955
## covNgeenLab=covNgeenLab_-                          13.455178
## covPlnnd=covPlnnd_-                                13.205739
## thnksNoCovVacc=thnksNoCovVacc_-                    12.945459
## rareNoWorr=rareNoWorr_-                            12.695184
## covNat=covNat_-                                    11.376680
## eldrNoBgDl=eldrNoBgDl_-                            11.158948
## chnsCovRcst=chnsCovRcst_-                          10.698520
## flu=flu_-                                          10.388872
## redIntWthChina=redIntWthChina_-                     8.098557
## stpCovStpImmi=stpCovStpImmi_-                       7.984348
## bgThrt=bgThrt_-                                     7.768830
## ctiusAsian=ctiusAsian_-                             5.706719
## afrDie=afrDie_-                                     3.934790
## nwsGdJbComCov=nwsGdJbComCov_-                       3.820771
## afrDie=afrDie_=                                     3.406257
## nwsGdJbComCov=nwsGdJbComCov_=                      -2.098745
## eldrNoBgDl=eldrNoBgDl_+                            -3.448360
## stpCovStpImmi=stpCovStpImmi_+                      -3.718150
## medOrgRecBstInt=medOrgRecBstInt_+                  -4.022613
## bgThrt=bgThrt_+                                    -4.482996
## rareNoWorr=rareNoWorr_+                            -4.660009
## bgThrt=bgThrt_=                                    -5.028468
## thnksNoCovVacc=thnksNoCovVacc_+                    -5.099640
## skeptInfoDocSci=skeptInfoDocSci_+                  -5.122195
## redIntWthChina=redIntWthChina_+                    -5.255574
## redIntWthChina=redIntWthChina_=                    -5.312244
## ctiusAsian=ctiusAsian_=                            -5.357257
## fllwRecMedOrgImp=fllwRecMedOrgImp_+                -5.360168
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+          -5.381239
## medOrgUntrust=medOrgUntrust_+                      -5.555731
## polBgDlIntrst=polBgDlIntrst_=                      -5.727456
## chnsCovRcst=chnsCovRcst_=                          -5.959551
## stpCovStpImmi=stpCovStpImmi_=                      -6.101003
## covNat=covNat_+                                    -6.150879
## covPlnnd=covPlnnd_+                                -6.288349
## afrDie=afrDie_+                                    -6.297043
## covVacEffRedVirus=covVacEffRedVirus_+              -6.420119
## scntFkNwsCov=scntFkNwsCov_+                        -7.017747
## polBgDlIntrst=polBgDlIntrst_+                      -7.062744
## chnsCovRcst=chnsCovRcst_+                          -7.178220
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+  -7.237246
## covNat=covNat_=                                    -7.311276
## covNgeenLab=covNgeenLab_+                          -8.161399
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+          -8.726707
## covNgeenLab=covNgeenLab_=                          -8.993714
## mdiaCovBgrDl=mdiaCovBgrDl_=                        -9.210682
## covNtSerPolSay=covNtSerPolSay_+                    -9.423014
## mdiaCovBgrDl=mdiaCovBgrDl_+                        -9.461897
## eldrNoBgDl=eldrNoBgDl_=                           -10.271813
## flu=flu_=                                         -10.848956
## covPlnnd=covPlnnd_=                               -10.938574
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_=         -10.976908
## medOrgUntrust=medOrgUntrust_=                     -11.201702
## rareNoWorr=rareNoWorr_=                           -11.409928
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_= -11.484554
## thnksNoCovVacc=thnksNoCovVacc_=                   -11.493297
## covVacEffRedVirus=covVacEffRedVirus_=             -11.785467
## scntFkNwsCov=scntFkNwsCov_=                       -11.970013
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_=         -12.261014
## medOrgRecBstInt=medOrgRecBstInt_=                 -12.604106
## fllwRecMedOrgImp=fllwRecMedOrgImp_=               -12.857935
## skeptInfoDocSci=skeptInfoDocSci_=                 -13.099772
## covNtSerPolSay=covNtSerPolSay_=                   -13.210816
## 
## $`2`
##                                                     Cla/Mod   Mod/Cla    Global
## covNtSerPolSay=covNtSerPolSay_=                   70.921986 62.111801 21.299094
## medOrgRecBstInt=medOrgRecBstInt_=                 58.000000 72.049689 30.211480
## scntFkNwsCov=scntFkNwsCov_=                       61.963190 62.732919 24.622356
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_=         70.338983 51.552795 17.824773
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_=         67.187500 53.416149 19.335347
## skeptInfoDocSci=skeptInfoDocSci_=                 64.705882 54.658385 20.543807
## mdiaCovBgrDl=mdiaCovBgrDl_=                       53.333333 69.565217 31.722054
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_= 53.365385 68.944099 31.419940
## covVacEffRedVirus=covVacEffRedVirus_=             61.538462 54.658385 21.601208
## fllwRecMedOrgImp=fllwRecMedOrgImp_=               59.477124 56.521739 23.111782
## medOrgUntrust=medOrgUntrust_=                     56.470588 59.627329 25.679758
## flu=flu_=                                         59.349593 45.341615 18.580060
## thnksNoCovVacc=thnksNoCovVacc_=                   72.727273 29.813665  9.969789
## covNgeenLab=covNgeenLab_=                         50.000000 53.416149 25.981873
## covPlnnd=covPlnnd_=                               58.181818 39.751553 16.616314
## polBgDlIntrst=polBgDlIntrst_=                     44.495413 60.248447 32.930514
## eldrNoBgDl=eldrNoBgDl_=                           59.000000 36.645963 15.105740
## rareNoWorr=rareNoWorr_=                           60.215054 34.782609 14.048338
## covNat=covNat_=                                   42.920354 60.248447 34.138973
## chnsCovRcst=chnsCovRcst_=                         49.606299 39.130435 19.184290
## stpCovStpImmi=stpCovStpImmi_=                     40.000000 47.204969 28.700906
## redIntWthChina=redIntWthChina_=                   40.243902 40.993789 24.773414
## ctiusAsian=ctiusAsian_=                           50.000000 22.981366 11.178248
## bgThrt=bgThrt_=                                   36.842105 43.478261 28.700906
## afrDie=afrDie_+                                   32.014388 55.279503 41.993958
## chnsCovRcst=chnsCovRcst_+                         39.622642 26.086957 16.012085
## nwsGdJbComCov=nwsGdJbComCov_=                     29.589041 67.080745 55.135952
## covNgeenLab=covNgeenLab_+                         36.842105 13.043478  8.610272
## mdiaCovBgrDl=mdiaCovBgrDl_+                       32.631579 19.254658 14.350453
## thnksNoCovVacc=thnksNoCovVacc_+                    0.000000  0.000000  2.416918
## nwsGdJbComCov=nwsGdJbComCov_-                     17.391304 19.875776 27.794562
## afrDie=afrDie_-                                   13.333333  9.937888 18.126888
## bgThrt=bgThrt_-                                   17.602041 42.857143 59.214502
## ctiusAsian=ctiusAsian_-                           20.767888 73.913043 86.555891
## redIntWthChina=redIntWthChina_-                   17.155756 47.204969 66.918429
## stpCovStpImmi=stpCovStpImmi_-                     16.105769 41.614907 62.839879
## thnksNoCovVacc=thnksNoCovVacc_-                   19.482759 70.186335 87.613293
## flu=flu_-                                         16.163793 46.583851 70.090634
## eldrNoBgDl=eldrNoBgDl_-                           17.830882 60.248447 82.175227
## covPlnnd=covPlnnd_-                               17.077799 55.900621 79.607251
## rareNoWorr=rareNoWorr_-                           17.837838 61.490683 83.836858
## covNat=covNat_-                                   11.173184 24.844720 54.078550
## chnsCovRcst=chnsCovRcst_-                         13.053613 34.782609 64.803625
## covNgeenLab=covNgeenLab_-                         12.471132 33.540373 65.407855
## covVacEffRedVirus=covVacEffRedVirus_-             13.729508 41.614907 73.716012
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_-         14.509804 45.962733 77.039275
## skeptInfoDocSci=skeptInfoDocSci_-                 14.000000 43.478261 75.528701
## fllwRecMedOrgImp=fllwRecMedOrgImp_-               13.496933 40.993789 73.867069
## polBgDlIntrst=polBgDlIntrst_-                      3.921569  6.211180 38.519637
## medOrgUntrust=medOrgUntrust_-                     11.264368 30.434783 65.709970
## scntFkNwsCov=scntFkNwsCov_-                       11.159737 31.677019 69.033233
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_-         11.578947 34.161491 71.752266
## medOrgRecBstInt=medOrgRecBstInt_-                  9.241706 24.223602 63.746224
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_-  6.788512 16.149068 57.854985
## covNtSerPolSay=covNtSerPolSay_-                   10.315789 30.434783 71.752266
## mdiaCovBgrDl=mdiaCovBgrDl_-                        5.042017 11.180124 53.927492
##                                                        p.value     v.test
## covNtSerPolSay=covNtSerPolSay_=                   1.643567e-42  13.664977
## medOrgRecBstInt=medOrgRecBstInt_=                 5.418494e-38  12.885695
## scntFkNwsCov=scntFkNwsCov_=                       1.635926e-34  12.252154
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_=         7.501051e-33  11.937994
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_=         7.507180e-32  11.744832
## skeptInfoDocSci=skeptInfoDocSci_=                 8.568002e-31  11.537189
## mdiaCovBgrDl=mdiaCovBgrDl_=                       8.768821e-31  11.535195
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_= 2.240442e-30  11.454191
## covVacEffRedVirus=covVacEffRedVirus_=             3.169614e-28  11.016849
## fllwRecMedOrgImp=fllwRecMedOrgImp_=               8.891498e-28  10.923585
## medOrgUntrust=medOrgUntrust_=                     3.441422e-27  10.800016
## flu=flu_=                                         7.282717e-21   9.369572
## thnksNoCovVacc=thnksNoCovVacc_=                   1.526726e-18   8.787686
## covNgeenLab=covNgeenLab_=                         2.775084e-18   8.720283
## covPlnnd=covPlnnd_=                               3.364411e-17   8.433165
## polBgDlIntrst=polBgDlIntrst_=                     1.490256e-16   8.257283
## eldrNoBgDl=eldrNoBgDl_=                           4.288605e-16   8.130121
## rareNoWorr=rareNoWorr_=                           9.026386e-16   8.039421
## covNat=covNat_=                                   4.306726e-15   7.845663
## chnsCovRcst=chnsCovRcst_=                         3.258738e-12   6.966089
## stpCovStpImmi=stpCovStpImmi_=                     7.566876e-09   5.777833
## redIntWthChina=redIntWthChina_=                   1.318014e-07   5.276323
## ctiusAsian=ctiusAsian_=                           3.719588e-07   5.082776
## bgThrt=bgThrt_=                                   3.618849e-06   4.632152
## afrDie=afrDie_+                                   9.946597e-05   3.891891
## chnsCovRcst=chnsCovRcst_+                         1.308351e-04   3.824881
## nwsGdJbComCov=nwsGdJbComCov_=                     4.323362e-04   3.519515
## covNgeenLab=covNgeenLab_+                         2.748697e-02   2.204532
## mdiaCovBgrDl=mdiaCovBgrDl_+                       4.725275e-02   1.984028
## thnksNoCovVacc=thnksNoCovVacc_+                   1.091212e-02  -2.545500
## nwsGdJbComCov=nwsGdJbComCov_-                     8.865527e-03  -2.617197
## afrDie=afrDie_-                                   1.268437e-03  -3.223027
## bgThrt=bgThrt_-                                   1.552325e-06  -4.804379
## ctiusAsian=ctiusAsian_-                           3.997675e-07  -5.069068
## redIntWthChina=redIntWthChina_-                   2.439588e-09  -5.965451
## stpCovStpImmi=stpCovStpImmi_-                     2.964488e-10  -6.300624
## thnksNoCovVacc=thnksNoCovVacc_-                   1.123394e-12  -7.114475
## flu=flu_-                                         4.282410e-13  -7.246322
## eldrNoBgDl=eldrNoBgDl_-                           5.290579e-15  -7.819804
## covPlnnd=covPlnnd_-                               6.217674e-16  -8.084975
## rareNoWorr=rareNoWorr_-                           1.867630e-16  -8.230289
## covNat=covNat_-                                   6.217452e-18  -8.628477
## chnsCovRcst=chnsCovRcst_-                         2.572096e-19  -8.985650
## covNgeenLab=covNgeenLab_-                         1.109366e-21  -9.566173
## covVacEffRedVirus=covVacEffRedVirus_-             3.043118e-24 -10.158308
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_-         2.287756e-24 -10.186092
## skeptInfoDocSci=skeptInfoDocSci_-                 5.459649e-25 -10.324511
## fllwRecMedOrgImp=fllwRecMedOrgImp_-               2.104669e-25 -10.415600
## polBgDlIntrst=polBgDlIntrst_-                     4.176342e-26 -10.568383
## medOrgUntrust=medOrgUntrust_-                     2.477537e-26 -10.617250
## scntFkNwsCov=scntFkNwsCov_-                       2.954075e-30 -11.430206
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_-         1.183900e-31 -11.706258
## medOrgRecBstInt=medOrgRecBstInt_-                 1.867567e-32 -11.861870
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 6.344308e-36 -12.512926
## covNtSerPolSay=covNtSerPolSay_-                   5.552352e-38 -12.883813
## mdiaCovBgrDl=mdiaCovBgrDl_-                       1.489914e-38 -12.984921
## 
## $`3`
##                                                      Cla/Mod   Mod/Cla
## covNtSerPolSay=covNtSerPolSay_+                   69.5652174 61.538462
## mdiaCovBgrDl=mdiaCovBgrDl_+                       43.1578947 78.846154
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+         50.8474576 57.692308
## scntFkNwsCov=scntFkNwsCov_+                       61.9047619 50.000000
## polBgDlIntrst=polBgDlIntrst_+                     24.3386243 88.461538
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+         64.7058824 42.307692
## covVacEffRedVirus=covVacEffRedVirus_+             67.7419355 40.384615
## thnksNoCovVacc=thnksNoCovVacc_+                   93.7500000 28.846154
## covNgeenLab=covNgeenLab_+                         45.6140351 50.000000
## skeptInfoDocSci=skeptInfoDocSci_+                 69.2307692 34.615385
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+ 38.0281690 51.923077
## covPlnnd=covPlnnd_+                               64.0000000 30.769231
## covNat=covNat_+                                   33.3333333 50.000000
## medOrgRecBstInt=medOrgRecBstInt_+                 47.5000000 36.538462
## fllwRecMedOrgImp=fllwRecMedOrgImp_+               70.0000000 26.923077
## medOrgUntrust=medOrgUntrust_+                     38.5964912 42.307692
## bgThrt=bgThrt_+                                   27.5000000 42.307692
## chnsCovRcst=chnsCovRcst_+                         23.5849057 48.076923
## rareNoWorr=rareNoWorr_=                           24.7311828 44.230769
## redIntWthChina=redIntWthChina_+                   30.9090909 32.692308
## nwsGdJbComCov=nwsGdJbComCov_+                     20.3539823 44.230769
## afrDie=afrDie_+                                   13.6690647 73.076923
## covPlnnd=covPlnnd_=                               20.0000000 42.307692
## flu=flu_+                                         22.6666667 32.692308
## eldrNoBgDl=eldrNoBgDl_+                           44.4444444 15.384615
## rareNoWorr=rareNoWorr_+                           50.0000000 13.461538
## fllwRecMedOrgImp=fllwRecMedOrgImp_=               16.3398693 48.076923
## thnksNoCovVacc=thnksNoCovVacc_=                   22.7272727 28.846154
## eldrNoBgDl=eldrNoBgDl_=                           19.0000000 36.538462
## stpCovStpImmi=stpCovStpImmi_+                     23.2142857 25.000000
## skeptInfoDocSci=skeptInfoDocSci_=                 15.4411765 40.384615
## flu=flu_=                                         15.4471545 36.538462
## covVacEffRedVirus=covVacEffRedVirus_=             12.5874126 34.615385
## nwsGdJbComCov=nwsGdJbComCov_-                      3.8043478 13.461538
## mdiaCovBgrDl=mdiaCovBgrDl_=                        3.8095238 15.384615
## afrDie=afrDie_=                                    3.4090909 17.307692
## polBgDlIntrst=polBgDlIntrst_=                      2.7522936 11.538462
## stpCovStpImmi=stpCovStpImmi_-                      4.8076923 38.461538
## chnsCovRcst=chnsCovRcst_-                          4.6620047 38.461538
## redIntWthChina=redIntWthChina_-                    4.5146727 38.461538
## medOrgRecBstInt=medOrgRecBstInt_-                  3.3175355 26.923077
## bgThrt=bgThrt_-                                    2.8061224 21.153846
## eldrNoBgDl=eldrNoBgDl_-                            4.5955882 48.076923
## covNat=covNat_-                                    2.2346369 15.384615
## flu=flu_-                                          3.4482759 30.769231
## medOrgUntrust=medOrgUntrust_-                      2.5287356 21.153846
## polBgDlIntrst=polBgDlIntrst_-                      0.0000000  0.000000
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_-          3.3333333 32.692308
## rareNoWorr=rareNoWorr_-                            3.9639640 42.307692
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_-          2.7368421 25.000000
## scntFkNwsCov=scntFkNwsCov_-                        2.4070022 21.153846
## covVacEffRedVirus=covVacEffRedVirus_-              2.6639344 25.000000
## fllwRecMedOrgImp=fllwRecMedOrgImp_-                2.6584867 25.000000
## mdiaCovBgrDl=mdiaCovBgrDl_-                        0.8403361  5.769231
## covNgeenLab=covNgeenLab_-                          1.8475751 15.384615
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_-  1.0443864  7.692308
## skeptInfoDocSci=skeptInfoDocSci_-                  2.6000000 25.000000
## thnksNoCovVacc=thnksNoCovVacc_-                    3.7931034 42.307692
## covPlnnd=covPlnnd_-                                2.6565465 26.923077
## covNtSerPolSay=covNtSerPolSay_-                    1.6842105 15.384615
##                                                      Global      p.value
## covNtSerPolSay=covNtSerPolSay_+                    6.948640 5.995436e-30
## mdiaCovBgrDl=mdiaCovBgrDl_+                       14.350453 8.121159e-29
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+          8.912387 7.339612e-22
## scntFkNwsCov=scntFkNwsCov_+                        6.344411 1.333504e-21
## polBgDlIntrst=polBgDlIntrst_+                     28.549849 5.101205e-21
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+          5.135952 1.220572e-18
## covVacEffRedVirus=covVacEffRedVirus_+              4.682779 2.203815e-18
## thnksNoCovVacc=thnksNoCovVacc_+                    2.416918 5.084634e-17
## covNgeenLab=covNgeenLab_+                          8.610272 5.375066e-17
## skeptInfoDocSci=skeptInfoDocSci_+                  3.927492 6.124722e-16
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+ 10.725076 3.302372e-15
## covPlnnd=covPlnnd_+                                3.776435 2.476126e-13
## covNat=covNat_+                                   11.782477 6.461131e-13
## medOrgRecBstInt=medOrgRecBstInt_+                  6.042296 1.463690e-12
## fllwRecMedOrgImp=fllwRecMedOrgImp_+                3.021148 1.615108e-12
## medOrgUntrust=medOrgUntrust_+                      8.610272 2.742141e-12
## bgThrt=bgThrt_+                                   12.084592 7.242746e-09
## chnsCovRcst=chnsCovRcst_+                         16.012085 1.442445e-08
## rareNoWorr=rareNoWorr_=                           14.048338 2.877745e-08
## redIntWthChina=redIntWthChina_+                    8.308157 1.056336e-07
## nwsGdJbComCov=nwsGdJbComCov_+                     17.069486 1.712948e-06
## afrDie=afrDie_+                                   41.993958 2.850537e-06
## covPlnnd=covPlnnd_=                               16.616314 4.705136e-06
## flu=flu_+                                         11.329305 1.592927e-05
## eldrNoBgDl=eldrNoBgDl_+                            2.719033 2.185660e-05
## rareNoWorr=rareNoWorr_+                            2.114804 2.954745e-05
## fllwRecMedOrgImp=fllwRecMedOrgImp_=               23.111782 4.156170e-05
## thnksNoCovVacc=thnksNoCovVacc_=                    9.969789 5.954591e-05
## eldrNoBgDl=eldrNoBgDl_=                           15.105740 6.935312e-05
## stpCovStpImmi=stpCovStpImmi_+                      8.459215 1.724815e-04
## skeptInfoDocSci=skeptInfoDocSci_=                 20.543807 6.784784e-04
## flu=flu_=                                         18.580060 1.461547e-03
## covVacEffRedVirus=covVacEffRedVirus_=             21.601208 2.450654e-02
## nwsGdJbComCov=nwsGdJbComCov_-                     27.794562 1.240060e-02
## mdiaCovBgrDl=mdiaCovBgrDl_=                       31.722054 6.192947e-03
## afrDie=afrDie_=                                   39.879154 3.329702e-04
## polBgDlIntrst=polBgDlIntrst_=                     32.930514 2.810906e-04
## stpCovStpImmi=stpCovStpImmi_-                     62.839879 2.327502e-04
## chnsCovRcst=chnsCovRcst_-                         64.803625 6.399249e-05
## redIntWthChina=redIntWthChina_-                   66.918429 1.376468e-05
## medOrgRecBstInt=medOrgRecBstInt_-                 63.746224 2.357740e-08
## bgThrt=bgThrt_-                                   59.214502 7.667446e-09
## eldrNoBgDl=eldrNoBgDl_-                           82.175227 5.116998e-09
## covNat=covNat_-                                   54.078550 2.631309e-09
## flu=flu_-                                         70.090634 1.307620e-09
## medOrgUntrust=medOrgUntrust_-                     65.709970 9.593280e-12
## polBgDlIntrst=polBgDlIntrst_-                     38.519637 2.685222e-12
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_-         77.039275 1.194209e-12
## rareNoWorr=rareNoWorr_-                           83.836858 7.036566e-13
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_-         71.752266 3.758182e-13
## scntFkNwsCov=scntFkNwsCov_-                       69.033233 1.571835e-13
## covVacEffRedVirus=covVacEffRedVirus_-             73.716012 2.230956e-14
## fllwRecMedOrgImp=fllwRecMedOrgImp_-               73.867069 1.775121e-14
## mdiaCovBgrDl=mdiaCovBgrDl_-                       53.927492 1.600029e-14
## covNgeenLab=covNgeenLab_-                         65.407855 1.343410e-14
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- 57.854985 3.418612e-15
## skeptInfoDocSci=skeptInfoDocSci_-                 75.528701 1.275918e-15
## thnksNoCovVacc=thnksNoCovVacc_-                   87.613293 1.226534e-16
## covPlnnd=covPlnnd_-                               79.607251 9.803130e-18
## covNtSerPolSay=covNtSerPolSay_-                   71.752266 1.231666e-18
##                                                      v.test
## covNtSerPolSay=covNtSerPolSay_+                   11.368581
## mdiaCovBgrDl=mdiaCovBgrDl_+                       11.138794
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_+          9.608805
## scntFkNwsCov=scntFkNwsCov_+                        9.547121
## polBgDlIntrst=polBgDlIntrst_+                      9.407078
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_+          8.812801
## covVacEffRedVirus=covVacEffRedVirus_+              8.746342
## thnksNoCovVacc=thnksNoCovVacc_+                    8.384721
## covNgeenLab=covNgeenLab_+                          8.378184
## skeptInfoDocSci=skeptInfoDocSci_+                  8.086811
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_+  7.878915
## covPlnnd=covPlnnd_+                                7.320194
## covNat=covNat_+                                    7.190380
## medOrgRecBstInt=medOrgRecBstInt_+                  7.077887
## fllwRecMedOrgImp=fllwRecMedOrgImp_+                7.064228
## medOrgUntrust=medOrgUntrust_+                      6.990343
## bgThrt=bgThrt_+                                    5.785197
## chnsCovRcst=chnsCovRcst_+                          5.668272
## rareNoWorr=rareNoWorr_=                            5.548716
## redIntWthChina=redIntWthChina_+                    5.316756
## nwsGdJbComCov=nwsGdJbComCov_+                      4.784639
## afrDie=afrDie_+                                    4.681306
## covPlnnd=covPlnnd_=                                4.577523
## flu=flu_+                                          4.315430
## eldrNoBgDl=eldrNoBgDl_+                            4.245028
## rareNoWorr=rareNoWorr_+                            4.176926
## fllwRecMedOrgImp=fllwRecMedOrgImp_=                4.098622
## thnksNoCovVacc=thnksNoCovVacc_=                    4.014603
## eldrNoBgDl=eldrNoBgDl_=                            3.978494
## stpCovStpImmi=stpCovStpImmi_+                      3.756245
## skeptInfoDocSci=skeptInfoDocSci_=                  3.398131
## flu=flu_=                                          3.182212
## covVacEffRedVirus=covVacEffRedVirus_=              2.249094
## nwsGdJbComCov=nwsGdJbComCov_-                     -2.500535
## mdiaCovBgrDl=mdiaCovBgrDl_=                       -2.737387
## afrDie=afrDie_=                                   -3.588199
## polBgDlIntrst=polBgDlIntrst_=                     -3.632131
## stpCovStpImmi=stpCovStpImmi_-                     -3.680529
## chnsCovRcst=chnsCovRcst_-                         -3.997583
## redIntWthChina=redIntWthChina_-                   -4.347582
## medOrgRecBstInt=medOrgRecBstInt_-                 -5.583464
## bgThrt=bgThrt_-                                   -5.775611
## eldrNoBgDl=eldrNoBgDl_-                           -5.843322
## covNat=covNat_-                                   -5.953087
## flu=flu_-                                         -6.066458
## medOrgUntrust=medOrgUntrust_-                     -6.812476
## polBgDlIntrst=polBgDlIntrst_-                     -6.993286
## polDwnplCovPlpLDngr=polDwnplCovPlpLDngr_-         -7.106039
## rareNoWorr=rareNoWorr_-                           -7.178723
## frGovUseCovMndtVacc=frGovUseCovMndtVacc_-         -7.263996
## scntFkNwsCov=scntFkNwsCov_-                       -7.380934
## covVacEffRedVirus=covVacEffRedVirus_-             -7.636564
## fllwRecMedOrgImp=fllwRecMedOrgImp_-               -7.665950
## mdiaCovBgrDl=mdiaCovBgrDl_-                       -7.679266
## covNgeenLab=covNgeenLab_-                         -7.701630
## mdiaUseCovMkTrmpRepLkBd=mdiaUseCovMkTrmpRepLkBd_- -7.874590
## skeptInfoDocSci=skeptInfoDocSci_-                 -7.996899
## thnksNoCovVacc=thnksNoCovVacc_-                   -8.280506
## covPlnnd=covPlnnd_-                               -8.576232
## covNtSerPolSay=covNtSerPolSay_-                   -8.811787
save(res_mfa_quali, res.hcpc, file='./data/mfa_and_classification_onqualidata.rdata')

Analysis

Boxplot of choice covmis per group
df_covmis$covqual_class <- res.hcpc$data.clust$clust
freq(df_covmis$covqual_class)
##     n    % val%
## 1 449 67.8 67.8
## 2 161 24.3 24.3
## 3  52  7.9  7.9
df_covmis %>%
  transmute_at(.vars = vars(starts_with("covmis")),
               ~fct_recode(.x %>% as_factor,
                                   "-"="1",
                                   "-"="2",
                                   "="="3",
                                   "="="4",
                                   "+"="5",
                                   "+"="6")) %>%
  cbind(df,.) %>%
  mutate(covqual_class=res.hcpc$data.clust$clust) %>%
  gather(key = "Covmis_var", value="Covmis_res", df_covmis %>% colnames %>% str_detect("^covmis") %>% which) %>%
  mutate(Covmis_var=str_remove(Covmis_var,"^covmis_"),
         Covmis_res=factor(Covmis_res,
                           levels = c("+","=","-"))) %>%
  ggplot(., aes(x = covqual_class, fill=Covmis_res)) +
  scale_fill_manual(values=c("green", "blue", "red")) +
  geom_bar(position='fill') +
  facet_wrap(~ Covmis_var, ncol = 5)

mean of choice covmis per group
df_mean_per_cat <- df_covmis %>%
  dplyr::select(matches("^covmis_")) %>%
  mutate(covmis_cat=df_covmis$covqual_class) %>%
  group_by(covmis_cat) %>%
  summarise_all(mean)
df_mean_per_cat
## # A tibble: 3 × 27
##   covmis_cat covmis_att_flu covmis_att_afrDie covmis_att_eldrN… covmis_att_rare…
##   <fct>               <dbl>             <dbl>             <dbl>            <dbl>
## 1 1                    1.86              3.69              1.37             1.26
## 2 2                    2.73              4.33              2.31             2.23
## 3 3                    3.67              5.04              2.79             2.87
## # … with 22 more variables: covmis_att_bgThrt <dbl>,
## #   covmis_cnsp_ctiusAsian <dbl>, covmis_cnsp_stpCovStpImmi <dbl>,
## #   covmis_cnsp_redIntWthChina <dbl>, covmis_cnsp_chnsCovRcst <dbl>,
## #   covmis_orgn_covPlnnd <dbl>, covmis_orgn_covNat <dbl>,
## #   covmis_orgn_covNgeenLab <dbl>, covmis_orgn_scntFkNwsCov <dbl>,
## #   covmis_pltc_polBgDlIntrst <dbl>, covmis_pltc_covNtSerPolSay <dbl>,
## #   covmis_pltc_polDwnplCovPlpLDngr <dbl>, covmis_cvrg_mdiaCovBgrDl <dbl>, …

questionr::freq(df_covmis$covmis_cat)
## [1] n    %    val%
## <0 rows> (or 0-length row.names)
df_covmis$HH_INCOME_TEXT <- fct_recode(df$HH_INCOME %>% as.character,
"Less than $10,000"="1",
"$10,000 to $30,000"="2",
"$30,000 to $50,000"="3",
"$50,000 to $70,000"="4",
"$70,000 to $100,000"="5",
"$100,000 to $200,000"="6",
"$200,000 to $500,000"="7",
"$500,000 or more"="8")
table(df_covmis$HH_INCOME_TEXT, df_covmis$covqual_class) %>% cprop %>% xtable::xtable()
## % latex table generated in R 4.2.0 by xtable 1.8-4 package
## % Tue Jun  7 15:12:24 2022
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrr}
##   \hline
##  & 1 & 2 & 3 & All \\ 
##   \hline
## Less than \$10,000 & 8.24 & 11.18 & 17.31 & 9.67 \\ 
##   \$10,000 to \$30,000 & 22.27 & 31.06 & 23.08 & 24.47 \\ 
##   \$30,000 to \$50,000 & 18.71 & 16.15 & 21.15 & 18.28 \\ 
##   \$50,000 to \$70,000 & 15.37 & 16.15 & 13.46 & 15.41 \\ 
##   \$70,000 to \$100,000 & 15.59 & 11.18 & 11.54 & 14.20 \\ 
##   \$100,000 to \$200,000 & 14.25 & 9.32 & 11.54 & 12.84 \\ 
##   \$200,000 to \$500,000 & 4.90 & 3.73 & 1.92 & 4.38 \\ 
##   \$500,000 or more & 0.67 & 1.24 & 0.00 & 0.76 \\ 
##   Total & 100.00 & 100.00 & 100.00 & 100.00 \\ 
##    \hline
## \end{tabular}
## \end{table}

Demo cluster

table(df_covmis$demo_class, df_covmis$covqual_class) %>% cprop
##        
##         1     2     3     All  
##   1      33.6  26.7  40.4  32.5
##   2      22.9  20.5  17.3  21.9
##   3      10.9   5.6   3.8   9.1
##   4       5.3   7.5   5.8   5.9
##   5       3.3   1.9   1.9   2.9
##   6      16.5  20.5  17.3  17.5
##   7       7.3  17.4  13.5  10.3
##   Total 100.0 100.0 100.0 100.0
table(df_covmis$demo_class, df_covmis$covqual_class) %>% t.test()
## 
##  One Sample t-test
## 
## data:  .
## t = 3.8595, df = 20, p-value = 0.0009769
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  14.48614 48.56148
## sample estimates:
## mean of x 
##  31.52381

Logistiaue regression

library(ordinal)
## 
## Attaching package: 'ordinal'
## The following object is masked from 'package:dplyr':
## 
##     slice
library(nnet)
library(GGally)
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
library(gtsummary)
library(ggeffects)
library(rstatix)
## 
## Attaching package: 'rstatix'
## The following object is masked from 'package:stats':
## 
##     filter

We’ll do it on the demo classification and then on the other demographic data

df_covmis$covqual_class <- factor(df_covmis$covqual_class, c("1","2","3"))
freq(df_covmis$covqual_class)
##     n    % val%
## 1 449 67.8 67.8
## 2 161 24.3 24.3
## 3  52  7.9  7.9
df_covmis$demo_class <- df_covmis$demo_class %>% as.factor()
regm <- clm(covqual_class ~ demo_class, data = df_covmis)
summary(regm)
## formula: covqual_class ~ demo_class
## data:    df_covmis
## 
##  link  threshold nobs logLik  AIC     niter max.grad cond.H 
##  logit flexible  662  -524.90 1065.80 5(0)  1.20e-10 6.9e+01
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## demo_class2 -0.08559    0.23406  -0.366  0.71460   
## demo_class3 -0.68189    0.36403  -1.873  0.06105 . 
## demo_class4  0.31483    0.35300   0.892  0.37246   
## demo_class5 -0.49409    0.58000  -0.852  0.39428   
## demo_class6  0.23243    0.24033   0.967  0.33348   
## demo_class7  0.78221    0.27097   2.887  0.00389 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Threshold coefficients:
##     Estimate Std. Error z value
## 1|2   0.8172     0.1488   5.491
## 2|3   2.5662     0.1920  13.363
tbl_regression(regm, exponentiate = TRUE)
Characteristic OR1 95% CI1 p-value
demo_class
1
2 0.92 0.58, 1.45 0.7
3 0.51 0.24, 1.00 0.061
4 1.37 0.67, 2.70 0.4
5 0.61 0.17, 1.75 0.4
6 1.26 0.79, 2.02 0.3
7 2.19 1.28, 3.72 0.004
1 OR = Odds Ratio, CI = Confidence Interval
ggcoef_model(
  regm,
  exponentiate = TRUE
)

plot(ggeffect(regm, "demo_class"))
## Package `effects` is not available, but needed for `ggeffect()`. Either install package `effects`, or use `ggpredict()`. Calling `ggpredict()` now.FALSE

df_covmis$covqual_class <-  df_covmis$covqual_class %>%
  as.character() %>%
  as.numeric()
ggpubr::ggboxplot(df_covmis, x="demo_class", y="covqual_class")

res.aov <- anova_test(covqual_class ~ demo_class, data = df_covmis)
## Coefficient covariances computed by hccm()
res.aov
## ANOVA Table (type II tests)
## 
##       Effect DFn DFd     F     p p<.05   ges
## 1 demo_class   6 655 2.665 0.015     * 0.024
pwc <- df_covmis %>% tukey_hsd(covqual_class ~ demo_class)
pwc
## # A tibble: 21 × 9
##    term  group1 group2 null.value estimate conf.low conf.high p.adj p.adj.signif
##  * <chr> <chr>  <chr>       <dbl>    <dbl>    <dbl>     <dbl> <dbl> <chr>       
##  1 demo… 1      2               0  -0.0436  -0.243     0.155  0.995 ns          
##  2 demo… 1      3               0  -0.179   -0.449     0.0916 0.445 ns          
##  3 demo… 1      4               0   0.0662  -0.256     0.388  0.997 ns          
##  4 demo… 1      5               0  -0.132   -0.575     0.311  0.975 ns          
##  5 demo… 1      6               0   0.0443  -0.169     0.258  0.996 ns          
##  6 demo… 1      7               0   0.222   -0.0353    0.480  0.143 ns          
##  7 demo… 2      3               0  -0.135   -0.419     0.149  0.799 ns          
##  8 demo… 2      4               0   0.110   -0.224     0.444  0.96  ns          
##  9 demo… 2      5               0  -0.0886  -0.540     0.363  0.997 ns          
## 10 demo… 2      6               0   0.0879  -0.143     0.319  0.92  ns          
## # … with 11 more rows
pwc <- pwc %>% add_xy_position(x = "demo_class")
ggpubr::ggboxplot(df_covmis, x = "demo_class", y = "covqual_class") +
  ggpubr::stat_pvalue_manual(pwc, hide.ns = TRUE) +
  labs(
    subtitle = get_test_label(res.aov, detailed = TRUE),
    caption = get_pwc_label(pwc)
    )

df_covmis$covqual_class <- factor(df_covmis$covqual_class, c("1","2","3"))
freq(df_covmis$covqual_class)
##     n    % val%
## 1 449 67.8 67.8
## 2 161 24.3 24.3
## 3  52  7.9  7.9
df_covmis$CONTINENT_BORN_TEXT_1 <- relevel(df_covmis$CONTINENT_BORN_TEXT_1 %>% as.factor(), "USA")
df_covmis$DOB_YEAR_PERIODE <- relevel(df_covmis$DOB_YEAR_PERIODE %>% as.factor(), "(1995,2005]")
df_covmis$EDUCATION_2_TEXT <- fct_recode(df$EDUCATION_1 %>% as.character,
          "No college degree"="1",
          "No college degree"="2",
          "No college degree"="3",
          "College degree"="4",
          "College degree"="5",
          "Graduate degree"="6",
          "Graduate degree"="7",
          "Graduate degree"="8")
df_covmis$EDUCATION_2_TEXT %>% freq()
##                     n    % val%
## No college degree 276 41.7 41.7
## College degree    257 38.8 38.8
## Graduate degree   129 19.5 19.5
regm <- multinom(covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + DOB_YEAR_PERIODE + SEX_TEXT + EDUCATION_2_TEXT, data = df_covmis)
## # weights:  72 (46 variable)
## initial  value 720.689661 
## iter  10 value 490.757326
## iter  20 value 484.003154
## iter  30 value 483.035043
## iter  40 value 482.776302
## iter  50 value 482.772030
## final  value 482.772018 
## converged
summary(regm)
## Call:
## multinom(formula = covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + 
##     HAS_LIVED_USA + DOB_YEAR_PERIODE + SEX_TEXT + EDUCATION_2_TEXT, 
##     data = df_covmis)
## 
## Coefficients:
##   (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 2  -0.7399172                    1.2026244        0.5992759
## 3  -3.5324654                    0.7623684        1.8405444
##   CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 2                             0.07737533                    46.93527
## 3                             1.60638178                    48.61955
##   CONTINENT_BORN_TEXT_1Central Eastern Europe
## 2                                  -0.1527146
## 3                                   0.1859194
##   CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 2                            -0.4923584                       -38.954510
## 3                             0.6260380                         1.942831
##   CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 2                          -0.679238                   -35.367263
## 3                         -24.807752                    -8.539058
##   CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 2                          -47.51973                          -0.9291578
## 3                          -24.75700                          -0.6124034
##   HAS_LIVED_USATRUE DOB_YEAR_PERIODE(1945,1955] DOB_YEAR_PERIODE(1955,1965]
## 2        -1.0091765                   0.1253643                   0.3094846
## 3         0.1452345                 -36.7817417                   0.1294950
##   DOB_YEAR_PERIODE(1965,1975] DOB_YEAR_PERIODE(1975,1985]
## 2                   0.2322808                 -0.06440325
## 3                  -0.8686300                  0.67046172
##   DOB_YEAR_PERIODE(1985,1995] SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 2                  0.06796774    0.6987227     -28.83694          -0.2369775
## 3                 -0.19805022    0.2537439     -20.26495         -38.0138254
##   EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 2                     -0.4025703                      -0.5461438
## 3                     -0.2790196                      -0.9337707
## 
## Std. Errors:
##   (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 2   0.5923249                    0.6616935        0.2946185
## 3   0.9877753                    1.2069043        0.5935465
##   CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 2                              0.6382346                   0.8548434
## 3                              0.9863467                   0.8548434
##   CONTINENT_BORN_TEXT_1Central Eastern Europe
## 2                                   0.6032795
## 3                                   0.8835034
##   CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 2                             0.5659516                     5.001539e-15
## 3                             0.9749935                     1.654742e+00
##   CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 2                       1.290244e+00                 6.760417e-16
## 3                       1.011613e-11                 1.411400e-05
##   CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 2                       9.792997e-16                           0.5907222
## 3                       9.046577e-12                           0.8994333
##   HAS_LIVED_USATRUE DOB_YEAR_PERIODE(1945,1955] DOB_YEAR_PERIODE(1955,1965]
## 2         0.5293139                8.570257e-01                   0.5030957
## 3         0.7990807                2.612310e-17                   0.7083435
##   DOB_YEAR_PERIODE(1965,1975] DOB_YEAR_PERIODE(1975,1985]
## 2                    0.493491                   0.3700348
## 3                    1.074976                   0.4547040
##   DOB_YEAR_PERIODE(1985,1995] SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 2                   0.2596986    0.2015267  3.469577e-15        1.138045e+00
## 3                   0.4169129    0.3154862  2.380610e-11        9.897664e-18
##   EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 2                      0.2335385                       0.3177511
## 3                      0.3556798                       0.5255403
## 
## Residual Deviance: 965.544 
## AIC: 1057.544
tbl_regression(regm, exponentiate = TRUE)
## ℹ Multinomial models have a different underlying structure than the models
## gtsummary was designed for. Other gtsummary functions designed to work with
## tbl_regression objects may yield unexpected results.
Characteristic OR1 95% CI1 p-value
2
EXPGRP_TEXT
Chinese
Non-Chinese Asian 3.33 0.91, 12.2 0.069
White 1.82 1.02, 3.24 0.042
CONTINENT_BORN_TEXT_1
USA
4Tigers and Japan 1.08 0.31, 3.77 >0.9
Africa 241,951,808,177,338,089,472 45,298,605,201,129,431,040, 1,292,328,477,231,431,548,928 <0.001
Central Eastern Europe 0.86 0.26, 2.80 0.8
Developping Asia 0.61 0.20, 1.85 0.4
Middle East 0.00 0.00, 0.00 <0.001
North America 0.51 0.04, 6.36 0.6
Oceania 0.00 0.00, 0.00 <0.001
South America 0.00 0.00, 0.00 <0.001
Western Europe 0.39 0.12, 1.26 0.12
HAS_LIVED_USA
FALSE
TRUE 0.36 0.13, 1.03 0.057
DOB_YEAR_PERIODE
(1995,2005]
(1945,1955] 1.13 0.21, 6.08 0.9
(1955,1965] 1.36 0.51, 3.65 0.5
(1965,1975] 1.26 0.48, 3.32 0.6
(1975,1985] 0.94 0.45, 1.94 0.9
(1985,1995] 1.07 0.64, 1.78 0.8
SEX_TEXT
Female
Male 2.01 1.35, 2.99 <0.001
Other 0.00 0.00, 0.00 <0.001
Transgender 0.79 0.08, 7.34 0.8
EDUCATION_2_TEXT
No college degree
College degree 0.67 0.42, 1.06 0.085
Graduate degree 0.58 0.31, 1.08 0.086
3
EXPGRP_TEXT
Chinese
Non-Chinese Asian 2.14 0.20, 22.8 0.5
White 6.30 1.97, 20.2 0.002
CONTINENT_BORN_TEXT_1
USA
4Tigers and Japan 4.98 0.72, 34.5 0.10
Africa 1,303,779,557,693,494,919,168 244,095,697,809,265,033,216, 6,963,830,785,693,286,662,144 <0.001
Central Eastern Europe 1.20 0.21, 6.80 0.8
Developping Asia 1.87 0.28, 12.6 0.5
Middle East 6.98 0.27, 179 0.2
North America 0.00 0.00, 0.00 <0.001
Oceania 0.00 0.00, 0.00 <0.001
South America 0.00 0.00, 0.00 <0.001
Western Europe 0.54 0.09, 3.16 0.5
HAS_LIVED_USA
FALSE
TRUE 1.16 0.24, 5.54 0.9
DOB_YEAR_PERIODE
(1995,2005]
(1945,1955] 0.00 0.00, 0.00 <0.001
(1955,1965] 1.14 0.28, 4.56 0.9
(1965,1975] 0.42 0.05, 3.45 0.4
(1975,1985] 1.96 0.80, 4.77 0.14
(1985,1995] 0.82 0.36, 1.86 0.6
SEX_TEXT
Female
Male 1.29 0.69, 2.39 0.4
Other 0.00 0.00, 0.00 <0.001
Transgender 0.00 0.00, 0.00 <0.001
EDUCATION_2_TEXT
No college degree
College degree 0.76 0.38, 1.52 0.4
Graduate degree 0.39 0.14, 1.10 0.076
1 OR = Odds Ratio, CI = Confidence Interval
ggcoef_multinom(
  regm,
  exponentiate = TRUE
)

f_normfactor <- function(v){
  res <- AMBI::f_normalisation(v)
  res <- res*3
  res <- cut(res, c(0,1,2,3), labels=c('weak','middle','high'))
  return(res)
}
df_covmis$neuroticism_qual <- f_normfactor(df_covmis$AMBI_BIG5_Neuroticism)
df_covmis$extraversion_qual <- f_normfactor(df_covmis$AMBI_BIG5_Extraversion)
df_covmis$openness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Openness)
df_covmis$conscientiousness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Conscientiousness)
df_covmis$agreeableness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Agreeableness)
df_covmis$covqual_class <- relevel(df_covmis$covqual_class %>% as.factor(), "2")


regm <- multinom(covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + DOB_YEAR_PERIODE + SEX_TEXT + EDUCATION_2_TEXT + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, data = df_covmis)
## # weights:  102 (66 variable)
## initial  value 715.196600 
## iter  10 value 457.299787
## iter  20 value 446.412279
## iter  30 value 444.615073
## iter  40 value 444.201677
## iter  50 value 444.039935
## iter  60 value 444.011632
## final  value 444.011492 
## converged
summary(regm)
## Call:
## multinom(formula = covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + 
##     HAS_LIVED_USA + DOB_YEAR_PERIODE + SEX_TEXT + EDUCATION_2_TEXT + 
##     neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + 
##     agreeableness_qual, data = df_covmis)
## 
## Coefficients:
##   (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 1    1.738012                   -1.1158803       -0.9598885
## 3   -3.663480                   -0.2070407        1.5813282
##   CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 1                             -0.3275979                   -19.12281
## 3                              1.8344488                   -12.54272
##   CONTINENT_BORN_TEXT_1Central Eastern Europe
## 1                                   0.0466284
## 3                                   0.3779654
##   CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 1                             0.4631373                         22.61849
## 3                             1.5124886                         24.71378
##   CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 1                          0.9716688                   16.7287749
## 3                        -10.3027941                   -0.5179976
##   CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 1                          15.228505                           0.8530807
## 3                          -3.830835                           0.1679522
##   HAS_LIVED_USATRUE DOB_YEAR_PERIODE(1945,1955] DOB_YEAR_PERIODE(1955,1965]
## 1          0.788691                  -0.1840107                  -0.3438741
## 3          1.004703                 -19.4402105                  -0.2933579
##   DOB_YEAR_PERIODE(1965,1975] DOB_YEAR_PERIODE(1975,1985]
## 1                 -0.06760224                   0.2610123
## 3                 -0.89486498                   0.9009455
##   DOB_YEAR_PERIODE(1985,1995] SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 1                  0.05080513   -0.5156186     15.579950           0.1985237
## 3                 -0.01288729   -0.4149011     -1.940545         -17.6515469
##   EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 1                     0.29195800                       0.3656797
## 3                     0.01285511                      -0.6797506
##   neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 1              0.8226438            0.7609507              -0.4316414
## 3              0.9514179            0.5052454               0.1870162
##   extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 1            0.07198186          -0.3984916         0.5463803
## 3            0.65784636          -0.8193364        -0.9644435
##   conscientiousness_qualmiddle conscientiousness_qualhigh
## 1                  -0.09763558                -0.02900892
## 3                  -0.00575730                 0.57150202
##   agreeableness_qualmiddle agreeableness_qualhigh
## 1               -1.2024486            -0.01251068
## 3                0.2723946             0.81165224
## 
## Std. Errors:
##   (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 1    1.083845                    0.6888784        0.3167776
## 3    2.003122                    1.2736420        0.7282255
##   CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 1                               0.660478                5.400441e-09
## 3                               1.144425                2.186272e-06
##   CONTINENT_BORN_TEXT_1Central Eastern Europe
## 1                                   0.6349121
## 3                                   0.9953854
##   CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 1                             0.5888515                        0.9152782
## 3                             1.1460802                        0.9152782
##   CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 1                       1.301694e+00                 3.301291e-08
## 3                       1.808779e-05                 9.562324e-10
##   CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 1                       9.158028e-08                           0.6166259
## 3                       1.155500e-09                           0.9949234
##   HAS_LIVED_USATRUE DOB_YEAR_PERIODE(1945,1955] DOB_YEAR_PERIODE(1955,1965]
## 1         0.5493611                9.241718e-01                   0.5535269
## 3         0.8797301                2.946859e-09                   0.8339505
##   DOB_YEAR_PERIODE(1965,1975] DOB_YEAR_PERIODE(1975,1985]
## 1                   0.5288291                   0.4009660
## 3                   1.1443785                   0.5527925
##   DOB_YEAR_PERIODE(1985,1995] SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 1                   0.2768421    0.2249110  1.118505e-08        1.165827e+00
## 3                   0.4700206    0.3786254  5.610860e-10        1.551336e-08
##   EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 1                      0.2494315                       0.3413472
## 3                      0.4086299                       0.5993647
##   neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 1              0.3446849            0.4147411               0.2984756
## 3              0.5588578            0.7046277               0.5148896
##   extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 1             0.4434444           0.4846603         0.5154640
## 3             0.7293981           0.7069991         0.7737271
##   conscientiousness_qualmiddle conscientiousness_qualhigh
## 1                    0.3360337                  0.4154541
## 3                    0.6036637                  0.6914744
##   agreeableness_qualmiddle agreeableness_qualhigh
## 1                 0.618170              0.6949397
## 3                 1.315696              1.4037369
## 
## Residual Deviance: 888.023 
## AIC: 1020.023
table(df_covmis$EDUCATION_2_TEXT, df_covmis$EXPGRP_TEXT) %>% lprop() %>% xtable::xtable()
## % latex table generated in R 4.2.0 by xtable 1.8-4 package
## % Tue Jun  7 15:13:01 2022
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrr}
##   \hline
##  & Chinese & Non-Chinese Asian & White & Total \\ 
##   \hline
## No college degree & 27.17 & 0.72 & 72.10 & 100.00 \\ 
##   College degree & 36.19 & 2.72 & 61.09 & 100.00 \\ 
##   Graduate degree & 44.19 & 4.65 & 51.16 & 100.00 \\ 
##   All & 33.99 & 2.27 & 63.75 & 100.00 \\ 
##    \hline
## \end{tabular}
## \end{table}
ggcoef_multinom(
  regm,
  exponentiate = TRUE
)

the variable on the age is long, we shall try to shorten it and cross it with education variable (young people without a college degree are socially different than older on without a college degree)

df_covmis$DOB_YEAR_PERIODE <- df_covmis$DOB_YEAR %>% cut(breaks = c(1944,1955,1965,1975,1985,1995,2005))
df_covmis$DOB_AGE_BRACKET <- fct_recode(df_covmis$DOB_YEAR_PERIODE %>% as.character,
          "-25y"="(1995,2005]",
          "25 - 45y"="(1985,1995]",
          "25 - 45y"="(1975,1985]",
          "+45y"="(1944,1955]", 
          "+45y"="(1955,1965]",
          "+45y"="(1965,1975]")
df_covmis <-  df_covmis %>% unite("age_education", c("DOB_AGE_BRACKET","EDUCATION_2_TEXT"))
df_covmis$age_education <- factor(df_covmis$age_education, levels=c('-25y_No college degree',
                                                    "25 - 45y_No college degree",
                                                    '+45y_No college degree', 
                                                    "-25y_College degree", 
                                                    "25 - 45y_College degree",
                                                    "+45y_College degree",
                                                    "-25y_Graduate degree",
                                                    "25 - 45y_Graduate degree",
                                                    "+45y_Graduate degree"))
regm <- multinom(covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + SEX_TEXT + age_education + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, data = df_covmis)
## # weights:  105 (68 variable)
## initial  value 716.295212 
## iter  10 value 453.008834
## iter  20 value 443.820709
## iter  30 value 441.905424
## iter  40 value 441.421874
## iter  50 value 441.268173
## iter  60 value 441.254707
## final  value 441.254611 
## converged
summary(regm)
## Call:
## multinom(formula = covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + 
##     HAS_LIVED_USA + SEX_TEXT + age_education + neuroticism_qual + 
##     extraversion_qual + openness_qual + conscientiousness_qual + 
##     agreeableness_qual, data = df_covmis)
## 
## Coefficients:
##   (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 1    1.858675                   -1.0437121       -0.9098322
## 3   -3.721566                   -0.2958922        1.6784329
##   CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 1                             -0.3486255                   -17.73722
## 3                              1.9114099                   -13.35145
##   CONTINENT_BORN_TEXT_1Central Eastern Europe
## 1                                 -0.02777399
## 3                                  0.44043085
##   CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 1                             0.4876404                         21.82408
## 3                             1.3881898                         23.64850
##   CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 1                           1.112134                   15.3896377
## 3                         -10.564591                   -0.5458462
##   CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 1                          14.352886                          0.91341919
## 3                          -4.005214                          0.05695805
##   HAS_LIVED_USATRUE SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 1         0.8112177   -0.5616381     14.665945           0.1890714
## 3         0.9705948   -0.4567149     -1.191577         -16.6749331
##   age_education25 - 45y_No college degree age_education+45y_No college degree
## 1                               -0.314218                          -0.9636291
## 3                                0.450312                          -0.4569733
##   age_education-25y_College degree age_education25 - 45y_College degree
## 1                       -0.1792953                            0.2960378
## 3                        0.2420838                            0.3449017
##   age_education+45y_College degree age_education-25y_Graduate degree
## 1                       1.15068438                        0.03518851
## 3                       0.04766783                       -0.28454612
##   age_education25 - 45y_Graduate degree age_education+45y_Graduate degree
## 1                             0.3763641                        -0.1172915
## 3                            -0.1192872                       -19.8537947
##   neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 1              0.8774775            0.8325559              -0.4442107
## 3              0.8788918            0.3739392               0.2101593
##   extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 1            0.01771684          -0.4495745         0.4855999
## 3            0.67781227          -0.8799866        -1.0053462
##   conscientiousness_qualmiddle conscientiousness_qualhigh
## 1                 -0.068907063                -0.02756163
## 3                  0.007027449                 0.63413752
##   agreeableness_qualmiddle agreeableness_qualhigh
## 1                -1.188226            0.008055108
## 3                 0.303773            0.733355256
## 
## Std. Errors:
##   (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite
## 1    1.086954                    0.6804273        0.3201156
## 3    2.018067                    1.2856348        0.7431532
##   CONTINENT_BORN_TEXT_14Tigers and Japan CONTINENT_BORN_TEXT_1Africa
## 1                              0.6639521                1.792701e-08
## 3                              1.1438946                1.369105e-06
##   CONTINENT_BORN_TEXT_1Central Eastern Europe
## 1                                   0.6366510
## 3                                   0.9981343
##   CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Middle East
## 1                             0.5906057                        0.9265872
## 3                             1.1620097                        0.9265872
##   CONTINENT_BORN_TEXT_1North America CONTINENT_BORN_TEXT_1Oceania
## 1                       1.303460e+00                 1.070377e-07
## 3                       1.636786e-05                 3.748516e-09
##   CONTINENT_BORN_TEXT_1South America CONTINENT_BORN_TEXT_1Western Europe
## 1                       1.995566e-07                           0.6160382
## 3                       2.694570e-09                           0.9852024
##   HAS_LIVED_USATRUE SEX_TEXTMale SEX_TEXTOther SEX_TEXTTransgender
## 1         0.5476758    0.2268507  2.627758e-08        1.185345e+00
## 3         0.8756592    0.3788061  1.731246e-09        3.664013e-08
##   age_education25 - 45y_No college degree age_education+45y_No college degree
## 1                               0.4213367                           0.5315782
## 3                               0.6094198                           0.8356615
##   age_education-25y_College degree age_education25 - 45y_College degree
## 1                        0.3268457                            0.3285704
## 3                        0.5814401                            0.5505411
##   age_education+45y_College degree age_education-25y_Graduate degree
## 1                        0.8270658                         0.6505728
## 3                        1.3422018                         1.2136692
##   age_education25 - 45y_Graduate degree age_education+45y_Graduate degree
## 1                             0.3831647                      7.260942e-01
## 3                             0.6744568                      4.221120e-09
##   neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 1              0.3455548            0.4144532               0.3015682
## 3              0.5556366            0.6985694               0.5154151
##   extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 1             0.4472320           0.4905583         0.5220824
## 3             0.7220587           0.7064663         0.7716673
##   conscientiousness_qualmiddle conscientiousness_qualhigh
## 1                    0.3375349                  0.4168743
## 3                    0.6064400                  0.6879759
##   agreeableness_qualmiddle agreeableness_qualhigh
## 1                0.6170652              0.6931013
## 3                1.2994331              1.3879572
## 
## Residual Deviance: 882.5092 
## AIC: 1018.509
ggcoef_multinom(
  regm,
  exponentiate = TRUE
)

Now we shall consider only two groups, those in the first and second group, and those in the third group.

df_covmis$covqual_class_2 <- fct_recode(df_covmis$covqual_class %>% as.character,
          "Believers"="1",
          "Believers"="2",
          "Skeptics"="3")
freq(df_covmis$covqual_class_2)
##             n    % val%
## Believers 610 92.1 92.1
## Skeptics   52  7.9  7.9
reg <- glm(covqual_class_2 ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + SEX_TEXT +
             age_education + neuroticism_qual + extraversion_qual + openness_qual + 
             conscientiousness_qual + agreeableness_qual, data = df_covmis, family = binomial(logit))
reg
## 
## Call:  glm(formula = covqual_class_2 ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + 
##     HAS_LIVED_USA + SEX_TEXT + age_education + neuroticism_qual + 
##     extraversion_qual + openness_qual + conscientiousness_qual + 
##     agreeableness_qual, family = binomial(logit), data = df_covmis)
## 
## Coefficients:
##                                 (Intercept)  
##                                    -5.75877  
##                EXPGRP_TEXTNon-Chinese Asian  
##                                     0.34362  
##                            EXPGRP_TEXTWhite  
##                                     2.34901  
##      CONTINENT_BORN_TEXT_14Tigers and Japan  
##                                     2.17530  
##                 CONTINENT_BORN_TEXT_1Africa  
##                                   -15.75584  
## CONTINENT_BORN_TEXT_1Central Eastern Europe  
##                                     0.33159  
##       CONTINENT_BORN_TEXT_1Developping Asia  
##                                     1.03774  
##            CONTINENT_BORN_TEXT_1Middle East  
##                                     2.54159  
##          CONTINENT_BORN_TEXT_1North America  
##                                   -14.66870  
##                CONTINENT_BORN_TEXT_1Oceania  
##                                   -12.38292  
##          CONTINENT_BORN_TEXT_1South America  
##                                   -14.99015  
##         CONTINENT_BORN_TEXT_1Western Europe  
##                                    -0.56002  
##                           HAS_LIVED_USATRUE  
##                                     0.43110  
##                                SEX_TEXTMale  
##                                    -0.08644  
##                               SEX_TEXTOther  
##                                   -13.61811  
##                         SEX_TEXTTransgender  
##                                   -15.28029  
##     age_education25 - 45y_No college degree  
##                                     0.64883  
##         age_education+45y_No college degree  
##                                     0.17247  
##            age_education-25y_College degree  
##                                     0.40673  
##        age_education25 - 45y_College degree  
##                                     0.13832  
##            age_education+45y_College degree  
##                                    -0.86501  
##           age_education-25y_Graduate degree  
##                                    -0.32793  
##       age_education25 - 45y_Graduate degree  
##                                    -0.39631  
##           age_education+45y_Graduate degree  
##                                   -15.51461  
##                      neuroticism_qualmiddle  
##                                     0.27063  
##                        neuroticism_qualhigh  
##                                    -0.20500  
##                     extraversion_qualmiddle  
##                                     0.51339  
##                       extraversion_qualhigh  
##                                     0.64802  
##                         openness_qualmiddle  
##                                    -0.59504  
##                           openness_qualhigh  
##                                    -1.38074  
##                conscientiousness_qualmiddle  
##                                     0.01918  
##                  conscientiousness_qualhigh  
##                                     0.61368  
##                    agreeableness_qualmiddle  
##                                     1.21817  
##                      agreeableness_qualhigh  
##                                     0.76502  
## 
## Degrees of Freedom: 651 Total (i.e. Null);  618 Residual
##   (10 observations deleted due to missingness)
## Null Deviance:       352.9 
## Residual Deviance: 307.2     AIC: 375.2
ggcoef_model(reg, exponentiate = TRUE)
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values

which(is.na(df_covmis$extraversion_qual))
## [1] 585
df_covmis[585,]
##     X.4 X.3 X.1   X Unnamed..0       StartDate         EndDate Status
## 585 585 585 585 584        586 6/11/2020 17:38 6/11/2020 18:27      0
##         IPAddress Progress Duration..in.seconds. Finished    RecordedDate
## 585 69.62.136.135      100                  2940        1 6/11/2020 18:27
##            ResponseId RecipientLastName RecipientFirstName RecipientEmail
## 585 R_1Fktpj7qrFl5Z5M                NA                 NA             NA
##     ExternalReference LocationLatitude LocationLongitude DistributionChannel
## 585                NA          38.4207         -121.3623           anonymous
##     UserLanguage Q_RecaptchaScore               PROLIFICID CONSENT EXPGRP
## 585           EN              0.9 5de53996227eed0b8dd9c44d       1      1
##     CHIN_SPECIFIC CHIN_SPECIFIC_6_TEXT NONASIA_SPECIFIC
## 585            NA                                    NA
##     NONASIA_SPECIFIC_19_TEXT NonWNonA_Specific NonWNonA_Specific_7_TEXT
## 585                       NA                NA                       NA
##     NonWNonA_Specific_14_TEXT OTHER_RACE_1 OTHER_RACE_2 OTHER_RACE_3
## 585                        NA          -99          -99          -99
##     OTHER_RACE_4 OTHER_RACE_5 OTHER_RACE_6 OTHER_RACE_7 OTHER_RACE_4_TEXT
## 585          -99          -99          -99          -99               -99
##     OTHER_RACE_5_TEXT OTHER_RACE_6_TEXT OTHER_RACE_7_TEXT DOB_1 DOB_2 DOB_3 SEX
## 585               -99               -99               -99     9    14    89   2
##     SEX_4_TEXT EDUCATION_1 EDUCATION_2 EDUCATION_3 COUNTRY_BORN
## 585        -99           4           4           3            1
##     COUNTRY_BORN_2_TEXT BIRTHCTRY_RESIDENCE OTHER_COUNTRIES.1_1_TEXT
## 585                 -99                  31                      -99
##     OTHER_COUNTRIES.1_1_1 OTHER_COUNTRIES.1_2_TEXT OTHER_COUNTRIES.1_2_1
## 585                   -99                      -99                   -99
##     OTHER_COUNTRIES.1_3_TEXT OTHER_COUNTRIES.1_3_1 OTHER_COUNTRIES.1_4_TEXT
## 585                      -99                   -99                      -99
##     OTHER_COUNTRIES.1_4_1 OTHER_COUNTRIES.1_5_TEXT OTHER_COUNTRIES.1_5_1
## 585                   -99                      -99                   -99
##     OTHER_COUNTRIES.1_6_TEXT OTHER_COUNTRIES.1_6_1 OTHER_COUNTRIES.1_7_TEXT
## 585                    -99.0                   -99                    -99.0
##     OTHER_COUNTRIES.1_7_1 OTHER_COUNTRIES.1_8_TEXT OTHER_COUNTRIES.1_8_1
## 585                   -99                    -99.0                   -99
##     FIRST_LANGUAGE FIRST_LANGUAGE_2_TEXT OTHER_LANGUAGES OCCUPATION HH_INCOME
## 585              1                   -99             -99       Cook         2
##     COVSCRN01 COVSCRN02_P COVSCRN03 COVSCRN04 COVSCRN04_1_TEXT COVSCRN05
## 585         3          NA        NA        NA               NA        NA
##     COVSCRN05_1_TEXT COVSCRN02_N BAI_TIME_First.Click BAI_TIME_Last.Click
## 585               NA          NA                 6.22              40.369
##     BAI_TIME_Page.Submit BAI_TIME_Click.Count BAI_1 BAI_2 BAI_3 BAI_4 BAI_5
## 585               41.317                   26     3     4     4     4     4
##     BAI_6 BAI_7 BAI_8 BAI_9 BAI_10 BAI_11 BAI_12 BAI_13 BAI_14 BAI_15 BAI_16
## 585     2     4     4     4      4      2      2      3      4      4      4
##     BAI_17 BAI_18 BAI_19 BAI_20 BAI_21 BDI_TIME_First.Click BDI_TIME_Last.Click
## 585      4      4      2      3      4                3.877             161.515
##     BDI_TIME_Page.Submit BDI_TIME_Click.Count BDI01 BDI02 BDI03 BDI04 BDI05
## 585              162.615                   47     4     4     4     4     4
##     BDI06 BDI07 BDI08 BDI09 BDI10 BDI11 BDI12 BDI13 BDI14 BDI15 BDI16 BDI17
## 585     4     4     4     4     3     3     4     4     4     3     4     4
##     BDI18 BDI19 BDI19a BDI20 BDI21 COVATT_1 COVATT_2 COVATT_3 COVATT_4 COVATT_5
## 585     3     2      1     4     4        1        6        1        1        6
##     COVCONSP_1 COVCONSP_2 COVCONSP_3 COVCONSP_4 COVORIGIN_1 COVORIGIN_2
## 585          1          1          1          6           1           6
##     COVORIGIN_3 COVORIGIN_4 COVPOLITICS_1 COVPOLITICS_2 COVPOLITICS_3
## 585           1           1             1             1             6
##     COVCOVERAGE_1 COVCOVERAGE_2 COVCOVERAGE_3 COVANTIVACC_1 COVANTIVACC_2
## 585             1             5             1             1             1
##     COVANTIVACC_3 COVMEDSKEP_1 COVMEDSKEP_2 COVMEDSKEP_3 COVMEDSKEP_4
## 585             6            1            1            6            6
##     AMBI_W1_TIME_First.Click AMBI_W1_TIME_Last.Click AMBI_W1_TIME_Page.Submit
## 585                     3.47                 171.162                   172.25
##     AMBI_W1_TIME_Click.Count AMBI_W1_1 AMBI_W1_2 AMBI_W1_3 AMBI_W1_4 AMBI_W1_5
## 585                       60         1         1         1         1         1
##     AMBI_W1_6 AMBI_W1_7 AMBI_W1_8 AMBI_W1_9 AMBI_W1_10 AMBI_W1_11 AMBI_W1_12
## 585         1         1         4         3          4          1          5
##     AMBI_W1_13 AMBI_W1_14 AMBI_W1_15 AMBI_W1_16 AMBI_W1_17 AMBI_W1_18
## 585          5          5          1          1          1          1
##     AMBI_W1_19 AMBI_W1_20 AMBI_W1_21 AMBI_W1_22 AMBI_W1_23 AMBI_W1_24
## 585          5          1          5          1          4          5
##     AMBI_W1_25 AMBI_W1_26 AMBI_W1_27 AMBI_W1_28 AMBI_W1_29 AMBI_W1_30
## 585          5          1          5          5          4          5
##     AMBI_W1_31 AMBI_W1_32 AMBI_W1_33 AMBI_W1_34 AMBI_W1_35 AMBI_W1_36
## 585          5          1          5          5          1          1
##     AMBI_W1_37 AMBI_W1_38 AMBI_W1_39 AMBI_W1_40 AMBI_W1_41 AMBI_W1_42
## 585          5          1          4          2          5          5
##     AMBI_W1_43 AMBI_W1_44 AMBI_W1_45 AMBI_W1_46 AMBI_W1_47 AMBI_W1_DO_1
## 585          5          5          5          2          2           15
##     AMBI_W1_DO_2 AMBI_W1_DO_3 AMBI_W1_DO_4 AMBI_W1_DO_5 AMBI_W1_DO_6
## 585           26           22           20           44           42
##     AMBI_W1_DO_7 AMBI_W1_DO_8 AMBI_W1_DO_9 AMBI_W1_DO_10 AMBI_W1_DO_11
## 585            2           13           10            46            28
##     AMBI_W1_DO_12 AMBI_W1_DO_13 AMBI_W1_DO_14 AMBI_W1_DO_15 AMBI_W1_DO_16
## 585             9            12            43            32             3
##     AMBI_W1_DO_17 AMBI_W1_DO_18 AMBI_W1_DO_19 AMBI_W1_DO_20 AMBI_W1_DO_21
## 585            41            11            16            30            17
##     AMBI_W1_DO_22 AMBI_W1_DO_23 AMBI_W1_DO_24 AMBI_W1_DO_25 AMBI_W1_DO_26
## 585            45            18            35            36            19
##     AMBI_W1_DO_27 AMBI_W1_DO_28 AMBI_W1_DO_29 AMBI_W1_DO_30 AMBI_W1_DO_31
## 585            29            38             4            31            40
##     AMBI_W1_DO_32 AMBI_W1_DO_33 AMBI_W1_DO_34 AMBI_W1_DO_35 AMBI_W1_DO_36
## 585            47             1            27            37             7
##     AMBI_W1_DO_37 AMBI_W1_DO_38 AMBI_W1_DO_39 AMBI_W1_DO_40 AMBI_W1_DO_41
## 585            21            25            34             8            23
##     AMBI_W1_DO_42 AMBI_W1_DO_43 AMBI_W1_DO_44 AMBI_W1_DO_45 AMBI_W1_DO_46
## 585             6             5            33            39            14
##     AMBI_W1_DO_47 V1.1_W_TIME_P1_First.Click V1.1_W_TIME_P1_Last.Click
## 585            24                         NA                        NA
##     V1.1_W_TIME_P1_Page.Submit V1.1_W_TIME_P1_Click.Count V1.1_W_JudgeOther_1
## 585                         NA                         NA                  NA
##     V1.1_W_JudgeOther_2 V1.1_W_JudgeOther_3 V1.1_W_JudgeOther_DO_1
## 585                  NA                  NA                     NA
##     V1.1_W_JudgeOther_DO_2 V1.1_W_JudgeOther_DO_3 V1.1_W_TIME_P2_First.Click
## 585                     NA                     NA                         NA
##     V1.1_W_TIME_P2_Last.Click V1.1_W_TIME_P2_Page.Submit
## 585                        NA                         NA
##     V1.1_W_TIME_P2_Click.Count V1.1_W_JudgeSelf_1 V1.1_W_JudgeSelf_2
## 585                         NA                 NA                 NA
##     V1.1_W_JudgeSelf_3 V1.1_W_JudgeSelf_DO_1 V1.1_W_JudgeSelf_DO_2
## 585                 NA                    NA                    NA
##     V1.1_W_JudgeSelf_DO_3 V1.2_TIME_P1_First.Click V1.2_TIME_P1_Last.Click
## 585                    NA                    5.829                  12.738
##     V1.2_TIME_P1_Page.Submit V1.2_TIME_P1_Click.Count V1.2_JudgeOther_1
## 585                   13.878                        6                 0
##     V1.2_JudgeOther_4 V1.2_JudgeOther_5 V1.2_JudgeOther_DO_1
## 585                 0                 0                    3
##     V1.2_JudgeOther_DO_4 V1.2_JudgeOther_DO_5 V1.2_TIME_P2_First.Click
## 585                    1                    2                    1.778
##     V1.2_TIME_P2_Last.Click V1.2_TIME_P2_Page.Submit V1.2_TIME_P2_Click.Count
## 585                   27.38                   28.469                        7
##     V1.2_JudgeSelf_1 V1.2_JudgeSelf_4 V1.2_JudgeSelf_5 V1.2_JudgeSelf_DO_1
## 585               47               48                0                   1
##     V1.2_JudgeSelf_DO_4 V1.2_JudgeSelf_DO_5 V1.1_B_TIME_P1_First.Click
## 585                   3                   2                      8.285
##     V1.1_B_TIME_P1_Last.Click V1.1_B_TIME_P1_Page.Submit
## 585                     17.57                     18.681
##     V1.1_B_TIME_P1_Click.Count V1.1_B_JudgeOther_1 V1.1_B_JudgeOther_2
## 585                          6                  51                  83
##     V1.1_B_JudgeOther_3 V1.1_B_JudgeOther_DO_1 V1.1_B_JudgeOther_DO_2
## 585                   0                      2                      3
##     V1.1_B_JudgeOther_DO_3 V1.1_B_TIME_P2_First.Click V1.1_B_TIME_P2_Last.Click
## 585                      1                     16.182                    24.196
##     V1.1_B_TIME_P2_Page.Submit V1.1_B_TIME_P2_Click.Count V1.1_B_JudgeSelf_1
## 585                     25.348                          7                 50
##     V1.1_B_JudgeSelf_2 V1.1_B_JudgeSelf_3 V1.1_B_JudgeSelf_DO_1
## 585                 49                  0                     2
##     V1.1_B_JudgeSelf_DO_2 V1.1_B_JudgeSelf_DO_3 V1.1_I_TIME_P1_First.Click
## 585                     1                     3                         NA
##     V1.1_I_TIME_P1_Last.Click V1.1_I_TIME_P1_Page.Submit
## 585                        NA                         NA
##     V1.1_I_TIME_P1_Click.Count V1.1_I_JudgeOther_1 V1.1_I_JudgeOther_2
## 585                         NA                  NA                  NA
##     V1.1_I_JudgeOther_3 V1.1_I_JudgeOther_DO_1 V1.1_I_JudgeOther_DO_2
## 585                  NA                     NA                     NA
##     V1.1_I_JudgeOther_DO_3 V1.1_I_TIME_P2_First.Click V1.1_I_TIME_P2_Last.Click
## 585                     NA                         NA                        NA
##     V1.1_I_TIME_P2_Page.Submit V1.1_I_TIME_P2_Click.Count V1.1_I_JudgeSelf_1
## 585                         NA                         NA                 NA
##     V1.1_I_JudgeSelf_2 V1.1_I_JudgeSelf_3 V1.1_I_JudgeSelf_DO_1
## 585                 NA                 NA                    NA
##     V1.1_I_JudgeSelf_DO_2 V1.1_I_JudgeSelf_DO_3 V1.1_C_TIME_P1_First.Click
## 585                    NA                    NA                         NA
##     V1.1_C_TIME_P1_Last.Click V1.1_C_TIME_P1_Page.Submit
## 585                        NA                         NA
##     V1.1_C_TIME_P1_Click.Count V1.1_C_JudgeOther_1 V1.1_C_JudgeOther_2
## 585                         NA                  NA                  NA
##     V1.1_C_JudgeOther_3 V1.1_C_JudgeOther_DO_1 V1.1_C_JudgeOther_DO_2
## 585                  NA                     NA                     NA
##     V1.1_C_JudgeOther_DO_3 V1.1_C_TIME_P2_First.Click V1.1_C_TIME_P2_Last.Click
## 585                     NA                         NA                        NA
##     V1.1_C_TIME_P2_Page.Submit V1.1_C_TIME_P2_Click.Count V1.1_C_JudgeSelf_1
## 585                         NA                         NA                 NA
##     V1.1_C_JudgeSelf_2 V1.1_C_JudgeSelf_3 V1.1_C_JudgeSelf_DO_1
## 585                 NA                 NA                    NA
##     V1.1_C_JudgeSelf_DO_2 V1.1_C_JudgeSelf_DO_3 AMBI_W2_TIME_First.Click
## 585                    NA                    NA                     7.99
##     AMBI_W2_TIME_Last.Click AMBI_W2_TIME_Page.Submit AMBI_W2_TIME_Click.Count
## 585                 129.124                  130.353                       63
##     AMBI_W2_1 AMBI_W2_2 AMBI_W2_3 AMBI_W2_4 AMBI_W2_5 AMBI_W2_6 AMBI_W2_7
## 585         1         4         5         1         1         5         1
##     AMBI_W2_8 AMBI_W2_9 AMBI_W2_10 AMBI_W2_11 AMBI_W2_12 AMBI_W2_13 AMBI_W2_14
## 585         1         1          1          5          5          2          5
##     AMBI_W2_15 AMBI_W2_16 AMBI_W2_17 AMBI_W2_18 AMBI_W2_19 AMBI_W2_20
## 585          5          1          5          4          5          4
##     AMBI_W2_21 AMBI_W2_22 AMBI_W2_23 AMBI_W2_24 AMBI_W2_25 AMBI_W2_26
## 585          5          5          5          1          5          4
##     AMBI_W2_27 AMBI_W2_28 AMBI_W2_29 AMBI_W2_30 AMBI_W2_31 AMBI_W2_32
## 585          1          1          1          1          5          5
##     AMBI_W2_33 AMBI_W2_34 AMBI_W2_35 AMBI_W2_36 AMBI_W2_37 AMBI_W2_38
## 585          4          5          2          5          5          4
##     AMBI_W2_39 AMBI_W2_40 AMBI_W2_41 AMBI_W2_42 AMBI_W2_43 AMBI_W2_44
## 585          1          1          2          2          1          1
##     AMBI_W2_45 AMBI_W2_46 AMBI_W2_47 AMBI_W2_DO_1 AMBI_W2_DO_2 AMBI_W2_DO_3
## 585          1          5          5           26            2           38
##     AMBI_W2_DO_4 AMBI_W2_DO_5 AMBI_W2_DO_6 AMBI_W2_DO_7 AMBI_W2_DO_8
## 585           36           13           19            6           40
##     AMBI_W2_DO_9 AMBI_W2_DO_10 AMBI_W2_DO_11 AMBI_W2_DO_12 AMBI_W2_DO_13
## 585           14            27             8            12            30
##     AMBI_W2_DO_14 AMBI_W2_DO_15 AMBI_W2_DO_16 AMBI_W2_DO_17 AMBI_W2_DO_18
## 585            32            24            10            47            18
##     AMBI_W2_DO_19 AMBI_W2_DO_20 AMBI_W2_DO_21 AMBI_W2_DO_22 AMBI_W2_DO_23
## 585            35            33            44             3            29
##     AMBI_W2_DO_24 AMBI_W2_DO_25 AMBI_W2_DO_26 AMBI_W2_DO_27 AMBI_W2_DO_28
## 585            43            37             7            21            45
##     AMBI_W2_DO_29 AMBI_W2_DO_30 AMBI_W2_DO_31 AMBI_W2_DO_32 AMBI_W2_DO_33
## 585            22            25            41            16            31
##     AMBI_W2_DO_34 AMBI_W2_DO_35 AMBI_W2_DO_36 AMBI_W2_DO_37 AMBI_W2_DO_38
## 585             1            34            39            46             4
##     AMBI_W2_DO_39 AMBI_W2_DO_40 AMBI_W2_DO_41 AMBI_W2_DO_42 AMBI_W2_DO_43
## 585             5            28            17            15            11
##     AMBI_W2_DO_44 AMBI_W2_DO_45 AMBI_W2_DO_46 AMBI_W2_DO_47
## 585            20            23             9            42
##     V2.1_B_TIME_P1_First.Click V2.1_B_TIME_P1_Last.Click
## 585                         NA                        NA
##     V2.1_B_TIME_P1_Page.Submit V2.1_B_TIME_P1_Click.Count V2.1_B_JudgeOther_1
## 585                         NA                         NA                  NA
##     V2.1_B_JudgeOther_2 V2.1_B_JudgeOther_3 V2.1_B_JudgeOther_DO_1
## 585                  NA                  NA                     NA
##     V2.1_B_JudgeOther_DO_2 V2.1_B_JudgeOther_DO_3 V2.1_B_TIME_P2_First.Click
## 585                     NA                     NA                         NA
##     V2.1_B_TIME_P2_Last.Click V2.1_B_TIME_P2_Page.Submit
## 585                        NA                         NA
##     V2.1_B_TIME_P2_Click.Count V2.1_B_JudgeSelf_1 V2.1_B_JudgeSelf_2
## 585                         NA                 NA                 NA
##     V2.1_B_JudgeSelf_3 V2.1_B_JudgeSelf_DO_1 V2.1_B_JudgeSelf_DO_2
## 585                 NA                    NA                    NA
##     V2.1_B_JudgeSelf_DO_3 V2.2_TIME_P1_First.Click V2.2_TIME_P1_Last.Click
## 585                    NA                    5.287                  13.407
##     V2.2_TIME_P1_Page.Submit V2.2_TIME_P1_Click.Count V2.2_JudgeOther_1
## 585                   14.497                        6                51
##     V2.2_JudgeOther_4 V2.2_JudgeOther_5 V2.2_JudgeOther_DO_1
## 585                51                92                    2
##     V2.2_JudgeOther_DO_4 V2.2_JudgeOther_DO_5 V2.2_TIME_P2_First.Click
## 585                    1                    3                     3.32
##     V2.2_TIME_P2_Last.Click V2.2_TIME_P2_Page.Submit V2.2_TIME_P2_Click.Count
## 585                  10.662                   11.586                        5
##     V2.2_JudgeSelf_1 V2.2_JudgeSelf_4 V2.2_JudgeSelf_5 V2.2_JudgeSelf_DO_1
## 585               53              100               99                   1
##     V2.2_JudgeSelf_DO_4 V2.2_JudgeSelf_DO_5 V2.1_W_TIME_P1_First.Click
## 585                   3                   2                      6.397
##     V2.1_W_TIME_P1_Last.Click V2.1_W_TIME_P1_Page.Submit
## 585                    15.027                     16.176
##     V2.1_W_TIME_P1_Click.Count V2.1_W_JudgeOther_1 V2.1_W_JudgeOther_2
## 585                          5                  78                  77
##     V2.1_W_JudgeOther_3 V2.1_W_JudgeOther_DO_1 V2.1_W_JudgeOther_DO_2
## 585                 100                      2                      1
##     V2.1_W_JudgeOther_DO_3 V2.1_W_TIME_P2_First.Click V2.1_W_TIME_P2_Last.Click
## 585                      3                     19.874                    32.168
##     V2.1_W_TIME_P2_Page.Submit V2.1_W_TIME_P2_Click.Count V2.1_W_JudgeSelf_1
## 585                     33.487                          5                100
##     V2.1_W_JudgeSelf_2 V2.1_W_JudgeSelf_3 V2.1_W_JudgeSelf_DO_1
## 585                100                100                     3
##     V2.1_W_JudgeSelf_DO_2 V2.1_W_JudgeSelf_DO_3 V2.1_C_TIME_P1_First.Click
## 585                     2                     1                         NA
##     V2.1_C_TIME_P1_Last.Click V2.1_C_TIME_P1_Page.Submit
## 585                        NA                         NA
##     V2.1_C_TIME_P1_Click.Count V2.1_C_JudgeOther_1 V2.1_C_JudgeOther_2
## 585                         NA                  NA                  NA
##     V2.1_C_JudgeOther_3 V2.1_C_JudgeOther_DO_1 V2.1_C_JudgeOther_DO_2
## 585                  NA                     NA                     NA
##     V2.1_C_JudgeOther_DO_3 V2.1_C_TIME_P2_First.Click V2.1_C_TIME_P2_Last.Click
## 585                     NA                         NA                        NA
##     V2.1_C_TIME_P2_Page.Submit V2.1_C_TIME_P2_Click.Count V2.1_C_JudgeSelf_1
## 585                         NA                         NA                 NA
##     V2.1_C_JudgeSelf_2 V2.1_C_JudgeSelf_3 V2.1_C_JudgeSelf_DO_1
## 585                 NA                 NA                    NA
##     V2.1_C_JudgeSelf_DO_2 V2.1_C_JudgeSelf_DO_3 V2.1_I_TIME_P1_First.Click
## 585                    NA                    NA                         NA
##     V2.1_I_TIME_P1_Last.Click V2.1_I_TIME_P1_Page.Submit
## 585                        NA                         NA
##     V2.1_I_TIME_P1_Click.Count V2.1_I_JudgeOther_1 V2.1_I_JudgeOther_2
## 585                         NA                  NA                  NA
##     V2.1_I_JudgeOther_3 V2.1_I_JudgeOther_DO_1 V2.1_I_JudgeOther_DO_2
## 585                  NA                     NA                     NA
##     V2.1_I_JudgeOther_DO_3 V2.1_I_TIME_P2_First.Click V2.1_I_TIME_P2_Last.Click
## 585                     NA                         NA                        NA
##     V2.1_I_TIME_P2_Page.Submit V2.1_I_TIME_P2_Click.Count V2.1_I_JudgeSelf_1
## 585                         NA                         NA                 NA
##     V2.1_I_JudgeSelf_2 V2.1_I_JudgeSelf_3 V2.1_I_JudgeSelf_DO_1
## 585                 NA                 NA                    NA
##     V2.1_I_JudgeSelf_DO_2 V2.1_I_JudgeSelf_DO_3 AMBI_W3_TIME_First.Click
## 585                    NA                    NA                    4.689
##     AMBI_W3_TIME_Last.Click AMBI_W3_TIME_Page.Submit AMBI_W3_TIME_Click.Count
## 585                 188.836                  189.996                       58
##     AMBI_W3_1 AMBI_W3_2 AMBI_W3_3 AMBI_W3_4 AMBI_W3_5 AMBI_W3_6 AMBI_W3_7
## 585         1         3         5         4         4         1         5
##     AMBI_W3_8 AMBI_W3_9 AMBI_W3_10 AMBI_W3_11 AMBI_W3_12 AMBI_W3_13 AMBI_W3_14
## 585         1         3          1          4          1          5          5
##     AMBI_W3_15 AMBI_W3_16 AMBI_W3_17 AMBI_W3_18 AMBI_W3_19 AMBI_W3_20
## 585          1          1          4          2          1          5
##     AMBI_W3_21 AMBI_W3_22 AMBI_W3_23 AMBI_W3_24 AMBI_W3_25 AMBI_W3_26
## 585          4          5          5          4          1          5
##     AMBI_W3_27 AMBI_W3_28 AMBI_W3_29 AMBI_W3_30 AMBI_W3_31 AMBI_W3_32
## 585          2          5          1          1          1          4
##     AMBI_W3_33 AMBI_W3_34 AMBI_W3_35 AMBI_W3_36 AMBI_W3_37 AMBI_W3_38
## 585          5          5          2          5          1          4
##     AMBI_W3_39 AMBI_W3_40 AMBI_W3_41 AMBI_W3_42 AMBI_W3_43 AMBI_W3_44
## 585          3          5          1          5          5          1
##     AMBI_W3_45 AMBI_W3_46 AMBI_W3_47 AMBI_W3_DO_1 AMBI_W3_DO_2 AMBI_W3_DO_3
## 585          5          5          3           39           47           34
##     AMBI_W3_DO_4 AMBI_W3_DO_5 AMBI_W3_DO_6 AMBI_W3_DO_7 AMBI_W3_DO_8
## 585           14           35           13            7           11
##     AMBI_W3_DO_9 AMBI_W3_DO_10 AMBI_W3_DO_11 AMBI_W3_DO_12 AMBI_W3_DO_13
## 585           22            20            31            26            17
##     AMBI_W3_DO_14 AMBI_W3_DO_15 AMBI_W3_DO_16 AMBI_W3_DO_17 AMBI_W3_DO_18
## 585            41             2            37             8            19
##     AMBI_W3_DO_19 AMBI_W3_DO_20 AMBI_W3_DO_21 AMBI_W3_DO_22 AMBI_W3_DO_23
## 585            24             3             5            44            30
##     AMBI_W3_DO_24 AMBI_W3_DO_25 AMBI_W3_DO_26 AMBI_W3_DO_27 AMBI_W3_DO_28
## 585            45            29            38            10            32
##     AMBI_W3_DO_29 AMBI_W3_DO_30 AMBI_W3_DO_31 AMBI_W3_DO_32 AMBI_W3_DO_33
## 585             6            25            46            18            15
##     AMBI_W3_DO_34 AMBI_W3_DO_35 AMBI_W3_DO_36 AMBI_W3_DO_37 AMBI_W3_DO_38
## 585            16            43            21            36            27
##     AMBI_W3_DO_39 AMBI_W3_DO_40 AMBI_W3_DO_41 AMBI_W3_DO_42 AMBI_W3_DO_43
## 585            42            28            23            12            40
##     AMBI_W3_DO_44 AMBI_W3_DO_45 AMBI_W3_DO_46 AMBI_W3_DO_47
## 585            33             9             4             1
##     V3.1_I_TIME_P1_First.Click V3.1_I_TIME_P1_Last.Click
## 585                         NA                        NA
##     V3.1_I_TIME_P1_Page.Submit V3.1_I_TIME_P1_Click.Count V3.1_I_JudgeOther_1
## 585                         NA                         NA                  NA
##     V3.1_I_JudgeOther_2 V3.1_I_JudgeOther_3 V3.1_I_JudgeOther_DO_1
## 585                  NA                  NA                     NA
##     V3.1_I_JudgeOther_DO_2 V3.1_I_JudgeOther_DO_3 V3.1_I_TIME_P2_First.Click
## 585                     NA                     NA                         NA
##     V3.1_I_TIME_P2_Last.Click V3.1_I_TIME_P2_Page.Submit
## 585                        NA                         NA
##     V3.1_I_TIME_P2_Click.Count V3.1_I_JudgeSelf_1 V3.1_I_JudgeSelf_2
## 585                         NA                 NA                 NA
##     V3.1_I_JudgeSelf_3 V3.1_I_JudgeSelf_DO_1 V3.1_I_JudgeSelf_DO_2
## 585                 NA                    NA                    NA
##     V3.1_I_JudgeSelf_DO_3 V3.2_TIME_P1_First.Click V3.2_TIME_P1_Last.Click
## 585                    NA                    4.523                   13.18
##     V3.2_TIME_P1_Page.Submit V3.2_TIME_P1_Click.Count V3.2_JudgeOther_1
## 585                   14.294                        6                54
##     V3.2_JudgeOther_4 V3.2_JudgeOther_5 V3.2_JudgeOther_DO_1
## 585                56                96                    1
##     V3.2_JudgeOther_DO_4 V3.2_JudgeOther_DO_5 V3.2_TIME_P2_First.Click
## 585                    2                    3                    2.469
##     V3.2_TIME_P2_Last.Click V3.2_TIME_P2_Page.Submit V3.2_TIME_P2_Click.Count
## 585                   18.81                   19.828                        6
##     V3.2_JudgeSelf_1 V3.2_JudgeSelf_4 V3.2_JudgeSelf_5 V3.2_JudgeSelf_DO_1
## 585               73               75              100                   1
##     V3.2_JudgeSelf_DO_4 V3.2_JudgeSelf_DO_5 V3.1_C_TIME_P1_First.Click
## 585                   3                   2                      3.076
##     V3.1_C_TIME_P1_Last.Click V3.1_C_TIME_P1_Page.Submit
## 585                   105.407                    106.231
##     V3.1_C_TIME_P1_Click.Count V3.1_C_JudgeOther_1 V3.1_C_JudgeOther_2
## 585                          7                  51                  51
##     V3.1_C_JudgeOther_3 V3.1_C_JudgeOther_DO_1 V3.1_C_JudgeOther_DO_2
## 585                 100                      3                      1
##     V3.1_C_JudgeOther_DO_3 V3.1_C_TIME_P2_First.Click V3.1_C_TIME_P2_Last.Click
## 585                      2                    102.609                   139.931
##     V3.1_C_TIME_P2_Page.Submit V3.1_C_TIME_P2_Click.Count V3.1_C_JudgeSelf_1
## 585                    140.865                          6                 51
##     V3.1_C_JudgeSelf_2 V3.1_C_JudgeSelf_3 V3.1_C_JudgeSelf_DO_1
## 585                 52                 96                     1
##     V3.1_C_JudgeSelf_DO_2 V3.1_C_JudgeSelf_DO_3 V3.1_W_TIME_P1_First.Click
## 585                     2                     3                         NA
##     V3.1_W_TIME_P1_Last.Click V3.1_W_TIME_P1_Page.Submit
## 585                        NA                         NA
##     V3.1_W_TIME_P1_Click.Count V3.1_W_JudgeOther_1 V3.1_W_JudgeOther_2
## 585                         NA                  NA                  NA
##     V3.1_W_JudgeOther_3 V3.1_W_JudgeOther_DO_1 V3.1_W_JudgeOther_DO_2
## 585                  NA                     NA                     NA
##     V3.1_W_JudgeOther_DO_3 V3.1_W_TIME_P2_First.Click V3.1_W_TIME_P2_Last.Click
## 585                     NA                         NA                        NA
##     V3.1_W_TIME_P2_Page.Submit V3.1_W_TIME_P2_Click.Count V3.1_W_JudgeSelf_1
## 585                         NA                         NA                 NA
##     V3.1_W_JudgeSelf_2 V3.1_W_JudgeSelf_3 V3.1_W_JudgeSelf_DO_1
## 585                 NA                 NA                    NA
##     V3.1_W_JudgeSelf_DO_2 V3.1_W_JudgeSelf_DO_3 V3.1_B_TIME_P1_First.Click
## 585                    NA                    NA                         NA
##     V3.1_B_TIME_P1_Last.Click V3.1_B_TIME_P1_Page.Submit
## 585                        NA                         NA
##     V3.1_B_TIME_P1_Click.Count V3.1_B_JudgeOther_1 V3.1_B_JudgeOther_2
## 585                         NA                  NA                  NA
##     V3.1_B_JudgeOther_3 V3.1_B_JudgeOther_DO_1 V3.1_B_JudgeOther_DO_2
## 585                  NA                     NA                     NA
##     V3.1_B_JudgeOther_DO_3 V3.1_B_TIME_P2_First.Click V3.1_B_TIME_P2_Last.Click
## 585                     NA                         NA                        NA
##     V3.1_B_TIME_P2_Page.Submit V3.1_B_TIME_P2_Click.Count V3.1_B_JudgeSelf_1
## 585                         NA                         NA                 NA
##     V3.1_B_JudgeSelf_2 V3.1_B_JudgeSelf_3 V3.1_B_JudgeSelf_DO_1
## 585                 NA                 NA                    NA
##     V3.1_B_JudgeSelf_DO_2 V3.1_B_JudgeSelf_DO_3 AMBI_W4_TIME_First.Click
## 585                    NA                    NA                    6.724
##     AMBI_W4_TIME_Last.Click AMBI_W4_TIME_Page.Submit AMBI_W4_TIME_Click.Count
## 585                 121.019                  122.085                       57
##     AMBI_W4_1 AMBI_W4_2 AMBI_W4_3 AMBI_W4_4 AMBI_W4_5 AMBI_W4_6 AMBI_W4_7
## 585         5         5         5         1         5         5         5
##     AMBI_W4_8 AMBI_W4_9 AMBI_W4_10 AMBI_W4_11 AMBI_W4_12 AMBI_W4_13 AMBI_W4_14
## 585         1         5          5          1          5          5          5
##     AMBI_W4_15 AMBI_W4_16 AMBI_W4_17 AMBI_W4_18 AMBI_W4_19 AMBI_W4_20
## 585          1          4          1          3          2          1
##     AMBI_W4_21 AMBI_W4_22 AMBI_W4_23 AMBI_W4_24 AMBI_W4_25 AMBI_W4_26
## 585          1          1          5          5          5          4
##     AMBI_W4_27 AMBI_W4_28 AMBI_W4_29 AMBI_W4_30 AMBI_W4_31 AMBI_W4_32
## 585          5          1          1          5          1          5
##     AMBI_W4_33 AMBI_W4_34 AMBI_W4_35 AMBI_W4_36 AMBI_W4_37 AMBI_W4_38
## 585          5          5          1          5          5          1
##     AMBI_W4_39 AMBI_W4_40 AMBI_W4_41 AMBI_W4_42 AMBI_W4_43 AMBI_W4_44
## 585          4          5          5          5          5          2
##     AMBI_W4_45 AMBI_W4_46 AMBI_W4_47 AMBI_W4_DO_1 AMBI_W4_DO_2 AMBI_W4_DO_3
## 585          5          5          1           17           35           42
##     AMBI_W4_DO_4 AMBI_W4_DO_5 AMBI_W4_DO_6 AMBI_W4_DO_7 AMBI_W4_DO_8
## 585           47           15            1           27           24
##     AMBI_W4_DO_9 AMBI_W4_DO_10 AMBI_W4_DO_11 AMBI_W4_DO_12 AMBI_W4_DO_13
## 585           16            11            10            19             2
##     AMBI_W4_DO_14 AMBI_W4_DO_15 AMBI_W4_DO_16 AMBI_W4_DO_17 AMBI_W4_DO_18
## 585             3            39            37            26             9
##     AMBI_W4_DO_19 AMBI_W4_DO_20 AMBI_W4_DO_21 AMBI_W4_DO_22 AMBI_W4_DO_23
## 585            44            21            29            32             5
##     AMBI_W4_DO_24 AMBI_W4_DO_25 AMBI_W4_DO_26 AMBI_W4_DO_27 AMBI_W4_DO_28
## 585            13             7            28            22             8
##     AMBI_W4_DO_29 AMBI_W4_DO_30 AMBI_W4_DO_31 AMBI_W4_DO_32 AMBI_W4_DO_33
## 585            40            23            46            30            45
##     AMBI_W4_DO_34 AMBI_W4_DO_35 AMBI_W4_DO_36 AMBI_W4_DO_37 AMBI_W4_DO_38
## 585             6            41            25             4            14
##     AMBI_W4_DO_39 AMBI_W4_DO_40 AMBI_W4_DO_41 AMBI_W4_DO_42 AMBI_W4_DO_43
## 585            43            38            18            31            34
##     AMBI_W4_DO_44 AMBI_W4_DO_45 AMBI_W4_DO_46 AMBI_W4_DO_47
## 585            33            36            12            20
##     V4.1_C_TIME_P1_First.Click V4.1_C_TIME_P1_Last.Click
## 585                         NA                        NA
##     V4.1_C_TIME_P1_Page.Submit V4.1_C_TIME_P1_Click.Count V4.1_C_JudgeOther_1
## 585                         NA                         NA                  NA
##     V4.1_C_JudgeOther_2 V4.1_C_JudgeOther_3 V4.1_C_JudgeOther_DO_1
## 585                  NA                  NA                     NA
##     V4.1_C_JudgeOther_DO_2 V4.1_C_JudgeOther_DO_3 V4.1_C_TIME_P2_First.Click
## 585                     NA                     NA                         NA
##     V4.1_C_TIME_P2_Last.Click V4.1_C_TIME_P2_Page.Submit
## 585                        NA                         NA
##     V4.1_C_TIME_P2_Click.Count V4.1_C_JudgeSelf_1 V4.1_C_JudgeSelf_2
## 585                         NA                 NA                 NA
##     V4.1_C_JudgeSelf_3 V4.1_C_JudgeSelf_DO_1 V4.1_C_JudgeSelf_DO_2
## 585                 NA                    NA                    NA
##     V4.1_C_JudgeSelf_DO_3 V4.2_TIME_P1_First.Click V4.2_TIME_P1_Last.Click
## 585                    NA                     5.57                  14.051
##     V4.2_TIME_P1_Page.Submit V4.2_TIME_P1_Click.Count V4.2_JudgeOther_1
## 585                   15.177                        5                53
##     V4.2_JudgeOther_4 V4.2_JudgeOther_5 V4.2_JudgeOther_DO_1
## 585                53                 4                    1
##     V4.2_JudgeOther_DO_4 V4.2_JudgeOther_DO_5 V4.2_TIME_P2_First.Click
## 585                    2                    3                    6.196
##     V4.2_TIME_P2_Last.Click V4.2_TIME_P2_Page.Submit V4.2_TIME_P2_Click.Count
## 585                  12.478                   13.757                        6
##     V4.2_JudgeSelf_1 V4.2_JudgeSelf_4 V4.2_JudgeSelf_5 V4.2_JudgeSelf_DO_1
## 585               53               52                0                   2
##     V4.2_JudgeSelf_DO_4 V4.2_JudgeSelf_DO_5 V4.1_I_TIME_P1_First.Click
## 585                   1                   3                      4.055
##     V4.1_I_TIME_P1_Last.Click V4.1_I_TIME_P1_Page.Submit
## 585                    15.433                     16.413
##     V4.1_I_TIME_P1_Click.Count V4.1_I_JudgeOther_1 V4.1_I_JudgeOther_2
## 585                          7                 100                 100
##     V4.1_I_JudgeOther_3 V4.1_I_JudgeOther_DO_1 V4.1_I_JudgeOther_DO_2
## 585                 100                      3                      1
##     V4.1_I_JudgeOther_DO_3 V4.1_I_TIME_P2_First.Click V4.1_I_TIME_P2_Last.Click
## 585                      2                      3.076                    12.534
##     V4.1_I_TIME_P2_Page.Submit V4.1_I_TIME_P2_Click.Count V4.1_I_JudgeSelf_1
## 585                      13.48                          6                100
##     V4.1_I_JudgeSelf_2 V4.1_I_JudgeSelf_3 V4.1_I_JudgeSelf_DO_1
## 585                100                100                     2
##     V4.1_I_JudgeSelf_DO_2 V4.1_I_JudgeSelf_DO_3 V4.1_B_TIME_P1_First.Click
## 585                     1                     3                         NA
##     V4.1_B_TIME_P1_Last.Click V4.1_B_TIME_P1_Page.Submit
## 585                        NA                         NA
##     V4.1_B_TIME_P1_Click.Count V4.1_B_JudgeOther_1 V4.1_B_JudgeOther_2
## 585                         NA                  NA                  NA
##     V4.1_B_JudgeOther_3 V4.1_B_JudgeOther_DO_1 V4.1_B_JudgeOther_DO_2
## 585                  NA                     NA                     NA
##     V4.1_B_JudgeOther_DO_3 V4.1_B_TIME_P2_First.Click V4.1_B_TIME_P2_Last.Click
## 585                     NA                         NA                        NA
##     V4.1_B_TIME_P2_Page.Submit V4.1_B_TIME_P2_Click.Count V4.1_B_JudgeSelf_1
## 585                         NA                         NA                 NA
##     V4.1_B_JudgeSelf_2 V4.1_B_JudgeSelf_3 V4.1_B_JudgeSelf_DO_1
## 585                 NA                 NA                    NA
##     V4.1_B_JudgeSelf_DO_2 V4.1_B_JudgeSelf_DO_3 V4.1_W_TIME_P1_First.Click
## 585                    NA                    NA                         NA
##     V4.1_W_TIME_P1_Last.Click V4.1_W_TIME_P1_Page.Submit
## 585                        NA                         NA
##     V4.1_W_TIME_P1_Click.Count V4.1_W_JudgeOther_1 V4.1_W_JudgeOther_2
## 585                         NA                  NA                  NA
##     V4.1_W_JudgeOther_3 V4.1_W_JudgeOther_DO_1 V4.1_W_JudgeOther_DO_2
## 585                  NA                     NA                     NA
##     V4.1_W_JudgeOther_DO_3 V4.1_W_TIME_P2_First.Click V4.1_W_TIME_P2_Last.Click
## 585                     NA                         NA                        NA
##     V4.1_W_TIME_P2_Page.Submit V4.1_W_TIME_P2_Click.Count V4.1_W_JudgeSelf_1
## 585                         NA                         NA                 NA
##     V4.1_W_JudgeSelf_2 V4.1_W_JudgeSelf_3 V4.1_W_JudgeSelf_DO_1
## 585                 NA                 NA                    NA
##     V4.1_W_JudgeSelf_DO_2 V4.1_W_JudgeSelf_DO_3 CHECK1 CHECK1_DO_1 CHECK1_DO_2
## 585                    NA                    NA      1           1           4
##     CHECK1_DO_3 CHECK1_DO_4 CHECK1_DO_5 CHECK2 CHECK2_DO_1 CHECK2_DO_2
## 585           3           2           5      1           4           1
##     CHECK2_DO_3 CHECK2_DO_4 CHECK3 CHECK3_DO_1 CHECK3_DO_2 CHECK3_DO_3
## 585           2           3      1           4           3           1
##     CHECK3_DO_4 CHECK3_DO_5                                  DEB1 DEB2_1 DEB2_2
## 585           2           5 Our mental health during the pandemic      1      0
##     DEB2_3 DEB2_4 DEB2_5 DEB2_6 DEB2_7 DEB2_8 DEB2_9 DEB2_10 DEB2_11 DEB2_12
## 585      1      0      0      0      0      1      1       0       1       0
##     DEB2_12_TEXT DEB2_DO_1 DEB2_DO_2 DEB2_DO_3 DEB2_DO_4 DEB2_DO_5 DEB2_DO_6
## 585          -99        11         1         5         6         8         2
##     DEB2_DO_7 DEB2_DO_8 DEB2_DO_9 DEB2_DO_10 DEB2_DO_11 DEB2_DO_12 DEB3_1
## 585         4         9         7         10          3         12      4
##     DEB3_2 DEB3_3 DEB3_4 DEB3_5 DEB3_6 DEB3_7 DEB3_8 DEB3_9 DEB3_10 DEB3_11
## 585     NA      3     NA     NA     NA     NA      5      1      NA       2
##     DEB3_12 DEB3_12_TEXT
## 585      NA             
##                                                                                                                                                                                                                                             DEB4
## 585 The pandemic has made my mental health plummet and I have pretty much spiraled into depression. I yearn to commit suicide, but I know that it would only cause trauma to the ones I love. I'm torn, but I know I don't want to live anymore.
##     Q_BallotBoxStuffing Q_RelevantIDDuplicate Q_RelevantIDDuplicateScore
## 585                  NA                    NA                         NA
##     Q_RelevantIDFraudScore             PROLIFIC_PID V_EthnicityOrder V1_Age
## 585                     NA 5de53996227eed0b8dd9c44d                2     32
##     V2_Age V3_Age V4_Age V1_Location V2_Location V3_Location V4_Location
## 585     39     43     33 in the city in the city in the city      nearby
##         V1_StoreType     V2_StoreType V3_StoreType V4_StoreType  V1_Name
## 585 department store department store  supermarket  supermarket Demetria
##     V2_Name V3_Name V4_Name
## 585  Pamela      Na   Madhu
##                                                                                                     V1_Framing
## 585 They happen to strike up a conversation with you, explaining to you that they are buying a large amount of
##                                                                                                     V2_Framing
## 585 They happen to strike up a conversation with you, explaining to you that they are buying a large amount of
##                                                                                                     V3_Framing
## 585 They happen to strike up a conversation with you, explaining to you that they are buying a large amount of
##                                                          V4_Framing
## 585 They wave to you and say that they are buying a large amount of
##     V_Pethnicity V_MainOrder V1_Product
## 585                 CTBH1122 cigarettes
##                                                                        V1_Presentation
## 585 they are stocking up for what they expect to be an anxiety-ridden couple of months
##       V2_Product
## 585 toilet paper
##                                                                        V2_Presentation
## 585 they are stocking up for what they expect to be an anxiety-ridden couple of months
##       V3_Product                                             V3_Presentation
## 585 baby formula they are out of formula and their baby cannot eat otherwise
##            V4_Product
## 585 hardware supplies
##                                                                                                                              V4_Presentation
## 585 their house has poor insulation and their roof is leaking, which is not safe for their family who are spending all of their time indoors
##               SurveyID OTHER_COUNTRIES.1_13_TEXT...Parent.Topics
## 585 SV_3pZs8qIp1fybqV7                                        NA
##     OTHER_COUNTRIES.1_13_TEXT...Sentiment.Polarity
## 585                                             NA
##     OTHER_COUNTRIES.1_13_TEXT...Sentiment.Score
## 585                                          NA
##     OTHER_COUNTRIES.1_13_TEXT...Sentiment OTHER_COUNTRIES.1_13_TEXT...Topics
## 585                                                                       NA
##     OTHER_COUNTRIES.1_13_TEXT...Topic.Sentiment.Label
## 585                                                NA
##     OTHER_COUNTRIES.1_13_TEXT...Topic.Sentiment.Score FL_172_DO_FL_173
## 585                                                NA               NA
##     FL_172_DO_FL_268 FL_172_DO_FL_267 FL_172_DO_FL_266 FL_172_DO_FL_265
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_264 FL_172_DO_FL_263 FL_172_DO_FL_262 FL_172_DO_FL_261
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_260 FL_172_DO_FL_259 FL_172_DO_FL_258 FL_172_DO_FL_257
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_256 FL_172_DO_FL_255 FL_172_DO_FL_254 FL_172_DO_FL_253
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_252 FL_172_DO_FL_251 FL_172_DO_FL_250 FL_172_DO_FL_249
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_248 FL_172_DO_FL_247 FL_172_DO_FL_246 FL_172_DO_FL_245
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_244 FL_172_DO_FL_243 FL_172_DO_FL_242 FL_172_DO_FL_241
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_240 FL_172_DO_FL_239 FL_172_DO_FL_238 FL_172_DO_FL_237
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_236 FL_172_DO_FL_235 FL_172_DO_FL_234 FL_172_DO_FL_233
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_232 FL_172_DO_FL_231 FL_172_DO_FL_230 FL_172_DO_FL_229
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_228 FL_172_DO_FL_227 FL_172_DO_FL_226 FL_172_DO_FL_225
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_224 FL_172_DO_FL_223 FL_172_DO_FL_222 FL_172_DO_FL_221
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_220 FL_172_DO_FL_219 FL_172_DO_FL_218 FL_172_DO_FL_217
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_216 FL_172_DO_FL_215 FL_172_DO_FL_214 FL_172_DO_FL_213
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_212 FL_172_DO_FL_211 FL_172_DO_FL_210 FL_172_DO_FL_209
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_208 FL_172_DO_FL_207 FL_172_DO_FL_206 FL_172_DO_FL_205
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_204 FL_172_DO_FL_203 FL_172_DO_FL_202 FL_172_DO_FL_201
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_200 FL_172_DO_FL_199 FL_172_DO_FL_198 FL_172_DO_FL_197
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_196 FL_172_DO_FL_195 FL_172_DO_FL_194 FL_172_DO_FL_193
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_192 FL_172_DO_FL_191 FL_172_DO_FL_190 FL_172_DO_FL_189
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_188 FL_172_DO_FL_187 FL_172_DO_FL_186 FL_172_DO_FL_185
## 585               NA               NA               NA                1
##     FL_172_DO_FL_184 FL_172_DO_FL_183 FL_172_DO_FL_182 FL_172_DO_FL_181
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_180 FL_172_DO_FL_179 FL_172_DO_FL_178 FL_172_DO_FL_177
## 585               NA               NA               NA               NA
##     FL_172_DO_FL_176 FL_172_DO_FL_175 FL_172_DO_FL_174 EXPGRP_TEXT
## 585               NA               NA               NA       White
##     CHIN_SPECIFIC_TEXT CONTINENT_BORN_TEXT_1 CONTINENT_BORN_TEXT_2
## 585               <NA>                   USA                   USA
##     CONTINENT_BORN_TEXT_3 HAS_LIVED_USA     HH_INCOME_TEXT DOB_YEAR
## 585                   USA          TRUE $10,000 to $30,000     1989
##     DOB_YEAR_PERIODE            EDUCATION_1_TEXT SEX_TEXT demo_class X.2 AMBI_1
## 585      (1985,1995] Associate degree in college   Female          1 585      1
##     AMBI_2 AMBI_3 AMBI_4 AMBI_5 AMBI_6 AMBI_7 AMBI_8 AMBI_9 AMBI_10 AMBI_11
## 585      1      1      1      1      1      1      4      3       4       1
##     AMBI_12 AMBI_13 AMBI_14 AMBI_15 AMBI_16 AMBI_17 AMBI_18 AMBI_19 AMBI_20
## 585       5       5       5       1       1       1       1       5       1
##     AMBI_21 AMBI_22 AMBI_23 AMBI_24 AMBI_25 AMBI_26 AMBI_27 AMBI_28 AMBI_29
## 585       5       1       4       5       5       1       5       5       4
##     AMBI_30 AMBI_31 AMBI_32 AMBI_33 AMBI_34 AMBI_35 AMBI_36 AMBI_37 AMBI_38
## 585       5       5       1       5       5       1       1       5       1
##     AMBI_39 AMBI_40 AMBI_41 AMBI_42 AMBI_43 AMBI_44 AMBI_45 AMBI_46 AMBI_47
## 585       4       2       5       5       5       5       5       2       1
##     AMBI_48 AMBI_49 AMBI_50 AMBI_51 AMBI_52 AMBI_53 AMBI_54 AMBI_55 AMBI_56
## 585       4       5       1       1       5       1       1       1       1
##     AMBI_57 AMBI_58 AMBI_59 AMBI_60 AMBI_61 AMBI_62 AMBI_63 AMBI_64 AMBI_65
## 585       5       5       2       5       5       1       5       4       5
##     AMBI_66 AMBI_67 AMBI_68 AMBI_69 AMBI_70 AMBI_71 AMBI_72 AMBI_73 AMBI_74
## 585       4       5       5       5       1       5       4       1       1
##     AMBI_75 AMBI_76 AMBI_77 AMBI_78 AMBI_79 AMBI_80 AMBI_81 AMBI_82 AMBI_83
## 585       5       5       5       2       5       5       4       1       1
##     AMBI_84 AMBI_85 AMBI_86 AMBI_87 AMBI_88 AMBI_89 AMBI_90 AMBI_91 AMBI_92
## 585       2       2       1       1       1       5       1       3       5
##     AMBI_93 AMBI_94 AMBI_95 AMBI_96 AMBI_97 AMBI_98 AMBI_99 AMBI_100 AMBI_101
## 585       4       4       1       5       1       3       1        4        1
##     AMBI_102 AMBI_103 AMBI_104 AMBI_105 AMBI_106 AMBI_107 AMBI_108 AMBI_109
## 585        5        5        1        1        4        2        1        5
##     AMBI_110 AMBI_111 AMBI_112 AMBI_113 AMBI_114 AMBI_115 AMBI_116 AMBI_117
## 585        4        5        5        4        1        5        2        5
##     AMBI_118 AMBI_119 AMBI_120 AMBI_121 AMBI_122 AMBI_123 AMBI_124 AMBI_125
## 585        1        1        1        4        5        5        2        5
##     AMBI_126 AMBI_127 AMBI_128 AMBI_129 AMBI_130 AMBI_131 AMBI_132 AMBI_133
## 585        1        4        3        5        1        5        5        1
##     AMBI_134 AMBI_135 AMBI_136 AMBI_137 AMBI_138 AMBI_139 AMBI_140 AMBI_141
## 585        5        5        5        5        5        1        5        5
##     AMBI_142 AMBI_143 AMBI_144 AMBI_145 AMBI_146 AMBI_147 AMBI_148 AMBI_149
## 585        5        1        5        5        1        5        5        5
##     AMBI_150 AMBI_151 AMBI_152 AMBI_153 AMBI_154 AMBI_155 AMBI_156 AMBI_157
## 585        1        4        1        3        2        1        1        1
##     AMBI_158 AMBI_159 AMBI_160 AMBI_161 AMBI_162 AMBI_163 AMBI_164 AMBI_165
## 585        5        5        5        4        5        1        1        5
##     AMBI_166 AMBI_167 AMBI_168 AMBI_169 AMBI_170 AMBI_171 AMBI_172 AMBI_173
## 585        1        5        5        5        1        5        5        1
##     AMBI_174 AMBI_175 AMBI_176 AMBI_177 AMBI_178 AMBI_179 AMBI_180 AMBI_181
## 585        4        5        5        5        5        2        5        5
##     AMBI_MSR_1_NEOPIR_ANXIETY AMBI_MSR_2_NEOPIR_ANGRYHOSTILITY
## 585                       0.8                              0.8
##     AMBI_MSR_3_NEOPIR_DEPRESSION AMBI_MSR_4_NEOPIR_SELFCONSCIOUSNESS
## 585                         0.76                                0.76
##     AMBI_MSR_5_NEOPIR_IMPULSIVENESS AMBI_MSR_6_NEOPIR_VULNERABILITY
## 585                            0.48                            0.76
##     AMBI_MSR_7_NEOPIR_WARMTH AMBI_MSR_8_NEOPIR_GREGARIOUSNESS
## 585                        0                                0
##     AMBI_MSR_9_NEOPIR_ASSERTIVENESS AMBI_MSR_10_NEOPIR_ACTIVITY
## 585                               0                           0
##     AMBI_MSR_11_NEOPIR_EXCITEMENTSEEKING AMBI_MSR_12_NEOPIR_POSITIVEEMOTIONS
## 585                                 0.32                                   0
##     AMBI_MSR_13_NEOPIR_FANTASY AMBI_MSR_14_NEOPIR_AESTHETICS
## 585                        0.6                           0.8
##     AMBI_MSR_15_NEOPIR_FEELINGS AMBI_MSR_16_NEOPIR_ACTIONS
## 585                        0.64                       0.44
##     AMBI_MSR_17_NEOPIR_IDEAS AMBI_MSR_18_NEOPIR_VALUES AMBI_MSR_19_NEOPIR_TRUST
## 585                     0.52                      0.68                     0.04
##     AMBI_MSR_20_NEOPIR_STRAIGHTFORWARDNESS AMBI_MSR_21_NEOPIR_ALTRUISM
## 585                                   0.52                        0.28
##     AMBI_MSR_22_NEOPIR_COMPLIANCE AMBI_MSR_23_NEOPIR_MODESTY
## 585                             0                        0.8
##     AMBI_MSR_24_NEOPIR_TENDERMINDEDNESS AMBI_MSR_25_NEOPIR_COMPETENCE
## 585                                0.64                          0.44
##     AMBI_MSR_26_NEOPIR_ORDER AMBI_MSR_27_NEOPIR_DUTIFULNESS
## 585                      0.8                           0.48
##     AMBI_MSR_28_NEOPIR_ACHIEVEMENTSTRIVING AMBI_MSR_29_NEOPIR_SELFDISCIPLINE
## 585                                   0.44                              0.72
##     AMBI_MSR_30_NEOPIR_DELIBERATION AMBI_MSR_31_HEXACOPI_SINCERITY
## 585                            0.64                           0.68
##     AMBI_MSR_32_HEXACOPI_FAIRNESS AMBI_MSR_33_HEXACOPI_GREEDAVOIDANCE
## 585                          0.44                                0.76
##     AMBI_MSR_34_HEXACOPI_MODESTY AMBI_MSR_35_HEXACOPI_FEARFULNESS
## 585                         0.76                             0.64
##     AMBI_MSR_36_HEXACOPI_ANXIETY AMBI_MSR_37_HEXACOPI_DEPENDENCE
## 585                          0.8                             0.2
##     AMBI_MSR_38_HEXACOPI_SENTIMENTALITY AMBI_MSR_39_HEXACOPI_EXPRESSIVENESS
## 585                                0.64                                0.08
##     AMBI_MSR_40_HEXACOPI_SOCIALBOLDNESS AMBI_MSR_41_HEXACOPI_SOCIABILITY
## 585                                   0                                0
##     AMBI_MSR_42_HEXACOPI_LIVELINESS AMBI_MSR_43_HEXACOPI_FORGIVENESS
## 585                               0                             0.16
##     AMBI_MSR_44_HEXACOPI_GENTLENESS AMBI_MSR_45_HEXACOPI_FLEXIBILITY
## 585                            0.24                             0.28
##     AMBI_MSR_46_HEXACOPI_PATIENCE AMBI_MSR_47_HEXACOPI_ORGANIZATION
## 585                          0.12                              0.76
##     AMBI_MSR_48_HEXACOPI_DILIGENCE AMBI_MSR_49_HEXACOPI_PERFECTIONISM
## 585                           0.68                               0.76
##     AMBI_MSR_50_HEXACOPI_PRUDENCE AMBI_MSR_51_HEXACOPI_AESTHETICAPPRECIATION
## 585                           0.8                                        0.8
##     AMBI_MSR_52_HEXACOPI_INQUISITIVENESS AMBI_MSR_53_HEXACOPI_CREATIVITY
## 585                                  0.8                            0.48
##     AMBI_MSR_54_HEXACOPI_UNCONVENTIONALITY AMBI_MSR_55_JPIR_COMPLEXITY
## 585                                   0.68                        0.64
##     AMBI_MSR_56_JPIR_BREADTHOFINTEREST AMBI_MSR_57_JPIR_INNOVATION
## 585                                0.8                        0.44
##     AMBI_MSR_58_JPIR_TOLERANCE AMBI_MSR_59_JPIR_EMPATHY
## 585                       0.16                     0.64
##     AMBI_MSR_60_JPIR_ANXIETY AMBI_MSR_61_JPIR_COOPERATIVENESS
## 585                      0.8                              0.6
##     AMBI_MSR_62_JPIR_SOCIABILITY AMBI_MSR_63_JPIR_SOCIALCONFIDENCE
## 585                         0.04                                 0
##     AMBI_MSR_64_JPIR_ENERGYLEVEL AMBI_MSR_65_JPIR_SOCIALASTUTENESS
## 585                         0.16                              0.28
##     AMBI_MSR_66_JPIR_RISKTAKING AMBI_MSR_67_JPIR_ORGANIZATION
## 585                         0.2                          0.72
##     AMBI_MSR_68_JPIR_TRADITIONALVALUES AMBI_MSR_69_JPIR_RESPONSIBILITY
## 585                                  0                            0.44
##     AMBI_MSR_70_MPQ_WELLBEING AMBI_MSR_71_MPQ_SOCIALPOTENCY
## 585                         0                             0
##     AMBI_MSR_72_MPQ_ACHIEVEMENT AMBI_MSR_73_MPQ_SOCIALCLOSENESS
## 585                        0.72                            0.04
##     AMBI_MSR_74_MPQ_STRESSREACTION AMBI_MSR_75_MPQ_AGGRESSION
## 585                            0.8                       0.48
##     AMBI_MSR_76_MPQ_ALIENATION AMBI_MSR_77_MPQ_CONTROL
## 585                        0.8                     0.8
##     AMBI_MSR_78_MPQ_HARMAVOIDANCE AMBI_MSR_79_MPQ_TRADITIONALISM
## 585                          0.64                           0.32
##     AMBI_MSR_80_MPQ_ABSORPTION AMBI_MSR_81_6FPQ_AFFILIATION
## 585                       0.48                            0
##     AMBI_MSR_82_6FPQ_DOMINANCE AMBI_MSR_83_6FPQ_EXHIBITION
## 585                          0                           0
##     AMBI_MSR_84_6FPQ_ABASEMENT AMBI_MSR_85_6FPQ_EVENTEMPERED
## 585                       0.08                             0
##     AMBI_MSR_86_6FPQ_GOODNATURED AMBI_MSR_87_6FPQ_COGNITIVESTRUCTURE
## 585                          0.2                                 0.8
##     AMBI_MSR_88_6FPQ_DELIBERATIVENESS AMBI_MSR_89_6FPQ_ORDER
## 585                               0.8                   0.76
##     AMBI_MSR_90_6FPQ_AUTONOMY AMBI_MSR_91_6FPQ_INDIVIDUALISM
## 585                       0.6                           0.36
##     AMBI_MSR_92_6FPQ_SELFRELIANCE AMBI_MSR_93_6FPQ_CHANGE
## 585                          0.52                    0.56
##     AMBI_MSR_94_6FPQ_UNDERSTANDING AMBI_MSR_95_6FPQ_BREADTHOFINTEREST
## 585                            0.8                                0.8
##     AMBI_MSR_96_6FPQ_ACHIEVEMENT AMBI_MSR_97_6FPQ_ENDURANCE
## 585                         0.72                       0.72
##     AMBI_MSR_98_6FPQ_SERIOUSNESS AMBI_MSR_99_TCI_EXPLORATORYEXCITABILITY
## 585                         0.24                                    0.56
##     AMBI_MSR_100_TCI_IMPULSIVENESS AMBI_MSR_101_TCI_EXTRAVAGANCE
## 585                              0                          0.64
##     AMBI_MSR_102_TCI_DISORDERLINESS AMBI_MSR_103_TCI_WORRY.PESSIMISM
## 585                            0.28                              0.8
##     AMBI_MSR_104_TCI_FEAROFUNCERTAINTY AMBI_MSR_105_TCI_SHYNESSWITHSTRANGERS
## 585                               0.64                                   0.8
##     AMBI_MSR_106_TCI_FATIGABILITY.ASTHENIA AMBI_MSR_107_TCI_SENTIMENTALITY
## 585                                   0.48                             0.6
##     AMBI_MSR_108_TCI_WARMCOMMUNICATION AMBI_MSR_109_TCI_ATTACHMENT
## 585                               0.12                        0.16
##     AMBI_MSR_110_TCI_DEPENDENCE AMBI_MSR_111_TCI_EAGERNESSOFEFFORT
## 585                        0.48                               0.56
##     AMBI_MSR_112_TCI_WORKHARDENED AMBI_MSR_113_TCI_AMBITIOUS
## 585                          0.68                       0.32
##     AMBI_MSR_114_TCI_PERFECTIONIST AMBI_MSR_115_TCI_RESPONSIBILITY
## 585                           0.76                               0
##     AMBI_MSR_116_TCI_PURPOSEFULNESS AMBI_MSR_117_TCI_RESOURCEFULNESS
## 585                               0                             0.08
##     AMBI_MSR_118_TCI_SELFACCEPTANCE AMBI_MSR_119_TCI_ENLIGHTENEDSECONDNATURE
## 585                             0.4                                     0.16
##     AMBI_MSR_120_TCI_SOCIALACCEPTANCE AMBI_MSR_121_TCI_EMPATHY
## 585                              0.44                     0.72
##     AMBI_MSR_122_TCI_HELPFULNESS AMBI_MSR_123_TCI_COMPASSION
## 585                         0.32                           0
##     AMBI_MSR_124_TCI_PUREHEARTEDCONSCIENCE AMBI_MSR_125_TCI_SELFFORGETFUL
## 585                                   0.24                           0.48
##     AMBI_MSR_126_TCI_TRANSPERSONALIDENTIFICATION
## 585                                         0.56
##     AMBI_MSR_127_TCI_SPIRITUALACCEPTANCE AMBI_MSR_128_TCI_ENLIGHTENED
## 585                                    0                            0
##     AMBI_MSR_129_TCI_IDEALISTIC AMBI_MSR_130_CPI_DOMINANCE
## 585                           0                          0
##     AMBI_MSR_131_CPI_CAPACITYFORSTATUS AMBI_MSR_132_CPI_SOCIABILITY
## 585                               0.04                            0
##     AMBI_MSR_133_CPI_SOCIALPRESENCE AMBI_MSR_134_CPI_SELFACCEPTANCE
## 585                            0.04                               0
##     AMBI_MSR_135_CPI_INDEPENDENCE AMBI_MSR_136_CPI_EMPATHY
## 585                          0.04                     0.04
##     AMBI_MSR_137_CPI_RESPONSIBILITY AMBI_MSR_138_CPI_SOCIALIZATION
## 585                            0.16                           0.12
##     AMBI_MSR_139_CPI_SELFCONTROL AMBI_MSR_140_CPI_GOODIMPRESSION
## 585                         0.44                               0
##     AMBI_MSR_141_CPI_COMMUNALITY AMBI_MSR_142_CPI_WELLBEING
## 585                         0.28                          0
##     AMBI_MSR_143_CPI_TOLERANCE AMBI_MSR_144_CPI_ACHIEVEMENTVIACONFORMANCE
## 585                       0.04                                       0.16
##     AMBI_MSR_145_CPI_ACHIEVEMENTVIAINDEPENDENCE
## 585                                        0.36
##     AMBI_MSR_146_CPI_INTELLECTUALEFFICIENCY
## 585                                    0.32
##     AMBI_MSR_147_CPI_PSYCHOLOGICALMINDEDNESS AMBI_MSR_148_CPI_FLEXIBILITY
## 585                                     0.32                         0.12
##     AMBI_MSR_149_CPI_FEMININITY AMBI_MSR_150_CPI_VECTOR1
## 585                        0.68                      0.8
##     AMBI_MSR_151_CPI_VECTOR2 AMBI_MSR_152_CPI_VECTOR3
## 585                     0.28                     0.08
##     AMBI_MSR_153_CPI_MANAGERIALPOTENTIAL AMBI_MSR_154_CPI_WORKORIENTATION
## 585                                    0                                0
##     AMBI_MSR_155_CPI_CREATIVETEMPERAMENT AMBI_MSR_156_CPI_LEADERSHIP
## 585                                 0.08                        0.04
##     AMBI_MSR_157_CPI_AMICABILITY AMBI_MSR_158_CPI_LAWENFORCEMENTORIENTATION
## 585                            0                                        0.6
##     AMBI_MSR_159_CPI_TOUGHMINDEDNESS AMBI_MSR_160_HPI_EMPATHY
## 585                             0.04                     0.12
##     AMBI_MSR_161_HPI_NOTANXIOUS AMBI_MSR_162_HPI_NOGUILT
## 585                           0                        0
##     AMBI_MSR_163_HPI_CALMNESS AMBI_MSR_164_HPI_EVENTEMPERED
## 585                         0                             0
##     AMBI_MSR_165_HPI_NOSOMATICCOMPLAINTS AMBI_MSR_166_HPI_TRUSTING
## 585                                    0                      0.04
##     AMBI_MSR_167_HPI_GOODATTACHMENT AMBI_MSR_168_HPI_COMPETITIVE
## 585                            0.04                         0.16
##     AMBI_MSR_169_HPI_SELFCONFIDENCE AMBI_MSR_170_HPI_NODEPRESSION
## 585                            0.04                             0
##     AMBI_MSR_171_HPI_LEADERSHIP AMBI_MSR_172_HPI_IDENTITY
## 585                           0                      0.16
##     AMBI_MSR_173_HPI_NOSOCIALANXIETY AMBI_MSR_174_HPI_LIKESPARTIES
## 585                             0.04                          0.16
##     AMBI_MSR_175_HPI_LIKESCROWDS AMBI_MSR_176_HPI_EXPERIENCESEEKING
## 585                         0.16                                0.6
##     AMBI_MSR_177_HPI_EXHIBITIONISTIC AMBI_MSR_178_HPI_ENTERTAINING
## 585                             0.04                           0.2
##     AMBI_MSR_179_HPI_EASYTOLIVEWITH AMBI_MSR_180_HPI_SENSITIVE
## 585                            0.12                       0.72
##     AMBI_MSR_181_HPI_CARING AMBI_MSR_182_HPI_LIKESPEOPLE
## 585                    0.44                            0
##     AMBI_MSR_183_HPI_NOHOSTILITY AMBI_MSR_184_HPI_MORALISTIC
## 585                         0.12                        0.28
##     AMBI_MSR_185_HPI_MASTERY AMBI_MSR_186_HPI_VIRTUOUS
## 585                     0.76                      0.16
##     AMBI_MSR_187_HPI_NOTAUTONOMOUS AMBI_MSR_188_HPI_NOTSPONTANEOUS
## 585                            0.6                            0.36
##     AMBI_MSR_189_HPI_IMPULSECONTROL AMBI_MSR_190_HPI_AVOIDSTROUBLE
## 585                             0.6                           0.12
##     AMBI_MSR_191_HPI_SCIENCEABILITY AMBI_MSR_192_HPI_CURIOSITY
## 585                             0.4                        0.4
##     AMBI_MSR_193_HPI_THRILLSEEKING AMBI_MSR_194_HPI_INTELLECTUALGAMES
## 585                           0.48                          0.6666667
##     AMBI_MSR_195_HPI_GENERATESIDEAS AMBI_MSR_196_HPI_CULTURE
## 585                            0.08                      0.8
##     AMBI_MSR_197_HPI_EDUCATION AMBI_MSR_198_HPI_MATHABILITY
## 585                       0.44                          0.4
##     AMBI_MSR_199_HPI_GOODMEMORY AMBI_MSR_200_HPI_READING
## 585                         0.6                     0.76
##     AMBI_MSR_201_HPI_SELFFOCUS AMBI_MSR_202_HPI_IMPRESSIONMANAGEMENT
## 585                       0.64                                  0.64
##     AMBI_MSR_203_HPI_APPEARANCE AMBI_MSR_EFA_ULS1 AMBI_MSR_EFA_ULS2
## 585                         0.6        -0.0258397       -0.02557284
##     AMBI_MSR_EFA_ULS3 AMBI_MSR_EFA_ULS5 AMBI_MSR_EFA_ULS4 AMBI_MSR_EFA_ULS6
## 585       0.009911969       -0.01754083        0.01474402       -0.01662301
##     AMBI_MSR_EFA_ULS9 AMBI_MSR_EFA_ULS8 AMBI_MSR_EFA_ULS13 AMBI_MSR_EFA_ULS10
## 585       -0.00153324      0.0009325445        0.007788777        -0.00716766
##     AMBI_MSR_EFA_ULS7 AMBI_MSR_EFA_ULS11 AMBI_MSR_EFA_ULS12
## 585       -0.01644725        0.005647905        -0.00138405
##     AMBI_BIG5_Neuroticism AMBI_BIG5_Extraversion AMBI_BIG5_Openness
## 585              2.257805              -2.948208           0.798661
##     AMBI_BIG5_Agreeableness AMBI_BIG5_Conscientiousness covmis_att_flu
## 585                  -2.312                   0.9416817              1
##     covmis_att_afrDie covmis_att_eldrNoBgDl covmis_att_rareNoWorr
## 585                 1                     1                     1
##     covmis_att_bgThrt covmis_cnsp_ctiusAsian covmis_cnsp_stpCovStpImmi
## 585                 1                      1                         1
##     covmis_cnsp_redIntWthChina covmis_cnsp_chnsCovRcst covmis_orgn_covPlnnd
## 585                          1                       1                    1
##     covmis_orgn_covNat covmis_orgn_covNgeenLab covmis_orgn_scntFkNwsCov
## 585                  1                       1                        1
##     covmis_pltc_polBgDlIntrst covmis_pltc_covNtSerPolSay
## 585                         1                          1
##     covmis_pltc_polDwnplCovPlpLDngr covmis_cvrg_mdiaCovBgrDl
## 585                               1                        1
##     covmis_cvrg_nwsGdJbComCov covmis_cvrg_mdiaUseCovMkTrmpRepLkBd
## 585                         2                                   1
##     covmis_anti_frGovUseCovMndtVacc covmis_anti_thnksNoCovVacc
## 585                               1                          1
##     covmis_anti_covVacEffRedVirus covmis_mdsk_medOrgUntrust
## 585                             1                         1
##     covmis_mdsk_skeptInfoDocSci covmis_mdsk_medOrgRecBstInt
## 585                           1                           1
##     covmis_mdsk_fllwRecMedOrgImp cov_class covqual_class
## 585                            1         1             1
##               age_education neuroticism_qual extraversion_qual openness_qual
## 585 25 - 45y_College degree             high              <NA>          high
##     conscientiousness_qual agreeableness_qual covqual_class_2
## 585                   high               weak       Believers
df_covmis2 <-  df_covmis %>% filter((!is.na(CONTINENT_BORN_TEXT_1))) %>%
  filter((!is.na(neuroticism_qual))) %>%
  filter((!is.na(extraversion_qual))) %>%
  filter((!is.na(openness_qual))) %>%
  filter((!is.na(conscientiousness_qual))) %>%
  filter((!is.na(agreeableness_qual)))
which(is.na(df_covmis2 %>% dplyr::select(EXPGRP_TEXT, CONTINENT_BORN_TEXT_1, HAS_LIVED_USA, SEX_TEXT,
             age_education, neuroticism_qual, extraversion_qual, openness_qual, 
             conscientiousness_qual, agreeableness_qual)), arr.ind=TRUE)
##      row col
df_covmis$CONTINENT_BORN_TEXT_1 %>% levels()
##  [1] "USA"                    "4Tigers and Japan"      "Africa"                
##  [4] "Central Eastern Europe" "Developping Asia"       "Middle East"           
##  [7] "North America"          "Oceania"                "South America"         
## [10] "Western Europe"
df_covmis$CONTINENT_BORN_TEXT_1 %>% levels()
##  [1] "USA"                    "4Tigers and Japan"      "Africa"                
##  [4] "Central Eastern Europe" "Developping Asia"       "Middle East"           
##  [7] "North America"          "Oceania"                "South America"         
## [10] "Western Europe"
reg <- glm(covqual_class_2 ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + SEX_TEXT +
             age_education + neuroticism_qual + extraversion_qual + openness_qual + 
             conscientiousness_qual + agreeableness_qual, 
           data = df_covmis2, 
           family = binomial(logit))
step(reg)
## Start:  AIC=375.18
## covqual_class_2 ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + 
##     SEX_TEXT + age_education + neuroticism_qual + extraversion_qual + 
##     openness_qual + conscientiousness_qual + agreeableness_qual
## 
##                          Df Deviance    AIC
## - age_education           8   315.04 367.04
## - CONTINENT_BORN_TEXT_1   9   318.14 368.14
## - SEX_TEXT                3   308.22 370.22
## - extraversion_qual       2   308.59 372.59
## - neuroticism_qual        2   308.70 372.70
## - HAS_LIVED_USA           1   307.47 373.47
## - agreeableness_qual      2   309.59 373.59
## - conscientiousness_qual  2   309.67 373.67
## <none>                        307.18 375.18
## - openness_qual           2   313.51 377.51
## - EXPGRP_TEXT             2   324.23 388.23
## 
## Step:  AIC=367.04
## covqual_class_2 ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + 
##     SEX_TEXT + neuroticism_qual + extraversion_qual + openness_qual + 
##     conscientiousness_qual + agreeableness_qual
## 
##                          Df Deviance    AIC
## - CONTINENT_BORN_TEXT_1   9   324.76 358.76
## - SEX_TEXT                3   316.05 362.05
## - extraversion_qual       2   315.84 363.84
## - neuroticism_qual        2   316.45 364.45
## - conscientiousness_qual  2   316.64 364.64
## - HAS_LIVED_USA           1   315.40 365.40
## - agreeableness_qual      2   317.77 365.77
## <none>                        315.04 367.04
## - openness_qual           2   321.24 369.24
## - EXPGRP_TEXT             2   334.18 382.18
## 
## Step:  AIC=358.76
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + SEX_TEXT + neuroticism_qual + 
##     extraversion_qual + openness_qual + conscientiousness_qual + 
##     agreeableness_qual
## 
##                          Df Deviance    AIC
## - SEX_TEXT                3   325.72 353.72
## - extraversion_qual       2   325.51 355.51
## - neuroticism_qual        2   326.00 356.00
## - agreeableness_qual      2   326.48 356.48
## - conscientiousness_qual  2   326.52 356.52
## - HAS_LIVED_USA           1   326.14 358.14
## <none>                        324.76 358.76
## - openness_qual           2   330.51 360.51
## - EXPGRP_TEXT             2   342.94 372.94
## 
## Step:  AIC=353.72
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + neuroticism_qual + 
##     extraversion_qual + openness_qual + conscientiousness_qual + 
##     agreeableness_qual
## 
##                          Df Deviance    AIC
## - extraversion_qual       2   326.61 350.61
## - neuroticism_qual        2   327.04 351.04
## - agreeableness_qual      2   327.36 351.36
## - conscientiousness_qual  2   327.46 351.46
## - HAS_LIVED_USA           1   327.04 353.04
## <none>                        325.72 353.72
## - openness_qual           2   331.60 355.60
## - EXPGRP_TEXT             2   343.90 367.90
## 
## Step:  AIC=350.61
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + neuroticism_qual + 
##     openness_qual + conscientiousness_qual + agreeableness_qual
## 
##                          Df Deviance    AIC
## - neuroticism_qual        2   328.17 348.17
## - agreeableness_qual      2   328.32 348.32
## - conscientiousness_qual  2   328.53 348.53
## - HAS_LIVED_USA           1   327.70 349.70
## <none>                        326.61 350.61
## - openness_qual           2   332.32 352.32
## - EXPGRP_TEXT             2   344.18 364.18
## 
## Step:  AIC=348.17
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + openness_qual + 
##     conscientiousness_qual + agreeableness_qual
## 
##                          Df Deviance    AIC
## - agreeableness_qual      2   330.29 346.29
## - conscientiousness_qual  2   330.57 346.57
## - HAS_LIVED_USA           1   329.10 347.10
## <none>                        328.17 348.17
## - openness_qual           2   333.99 349.99
## - EXPGRP_TEXT             2   344.91 360.91
## 
## Step:  AIC=346.29
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + openness_qual + 
##     conscientiousness_qual
## 
##                          Df Deviance    AIC
## - conscientiousness_qual  2   332.11 344.11
## - HAS_LIVED_USA           1   331.00 345.00
## <none>                        330.29 346.29
## - openness_qual           2   336.76 348.76
## - EXPGRP_TEXT             2   346.38 358.38
## 
## Step:  AIC=344.11
## covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + openness_qual
## 
##                 Df Deviance    AIC
## - HAS_LIVED_USA  1   333.39 343.39
## <none>               332.11 344.11
## - openness_qual  2   337.60 345.60
## - EXPGRP_TEXT    2   349.40 357.40
## 
## Step:  AIC=343.39
## covqual_class_2 ~ EXPGRP_TEXT + openness_qual
## 
##                 Df Deviance    AIC
## <none>               333.39 343.39
## - openness_qual  2   338.61 344.61
## - EXPGRP_TEXT    2   349.54 355.54
## 
## Call:  glm(formula = covqual_class_2 ~ EXPGRP_TEXT + openness_qual, 
##     family = binomial(logit), data = df_covmis2)
## 
## Coefficients:
##                  (Intercept)  EXPGRP_TEXTNon-Chinese Asian  
##                      -3.0838                        0.9851  
##             EXPGRP_TEXTWhite           openness_qualmiddle  
##                       1.5320                       -0.3869  
##            openness_qualhigh  
##                      -1.0616  
## 
## Degrees of Freedom: 651 Total (i.e. Null);  647 Residual
## Null Deviance:       352.9 
## Residual Deviance: 333.4     AIC: 343.4
reg <- glm(covqual_class_2 ~ EXPGRP_TEXT + HAS_LIVED_USA + openness_qual, 
           data = df_covmis2, 
           family = binomial(logit))
ggcoef_model(reg, exponentiate = TRUE)

df_covmis2$EDUCATION_2_TEXT <- fct_recode(df_covmis2$EDUCATION_1 %>% as.character,
          "No college degree"="1",
          "No college degree"="2",
          "No college degree"="3",
          "College degree"="4",
          "College degree"="5",
          "Graduate degree"="6",
          "Graduate degree"="7",
          "Graduate degree"="8")
df_covmis2$DOB_AGE_BRACKET <- fct_recode(df_covmis2$DOB_YEAR_PERIODE %>% as.character,
          "-25y"="(1995,2005]",
          "25 - 45y"="(1985,1995]",
          "25 - 45y"="(1975,1985]",
          "+45y"="(1944,1955]", 
          "+45y"="(1955,1965]",
          "+45y"="(1965,1975]")
regm <- multinom(covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + SEX_TEXT +
             EDUCATION_2_TEXT + DOB_AGE_BRACKET + neuroticism_qual + extraversion_qual + openness_qual + 
             conscientiousness_qual + agreeableness_qual, 
           data = df_covmis2)
## # weights:  93 (60 variable)
## initial  value 716.295212 
## iter  10 value 462.188017
## iter  20 value 449.856776
## iter  30 value 447.444923
## iter  40 value 446.934218
## iter  50 value 446.724179
## iter  60 value 446.717265
## final  value 446.717240 
## converged
step(regm)
## Start:  AIC=1013.43
## covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HAS_LIVED_USA + 
##     SEX_TEXT + EDUCATION_2_TEXT + DOB_AGE_BRACKET + neuroticism_qual + 
##     extraversion_qual + openness_qual + conscientiousness_qual + 
##     agreeableness_qual
## 
## trying - EXPGRP_TEXT 
## # weights:  87 (56 variable)
## initial  value 716.295212 
## iter  10 value 472.822904
## iter  20 value 463.510030
## iter  30 value 461.876855
## iter  40 value 461.580380
## iter  50 value 461.455216
## iter  60 value 461.452175
## final  value 461.452165 
## converged
## trying - CONTINENT_BORN_TEXT_1 
## # weights:  66 (42 variable)
## initial  value 716.295212 
## iter  10 value 476.303301
## iter  20 value 458.769836
## iter  30 value 457.522823
## iter  40 value 457.361151
## iter  50 value 457.346844
## final  value 457.346788 
## converged
## trying - HAS_LIVED_USA 
## # weights:  90 (58 variable)
## initial  value 716.295212 
## iter  10 value 464.157904
## iter  20 value 450.973604
## iter  30 value 448.654999
## iter  40 value 448.105546
## iter  50 value 447.976268
## iter  60 value 447.972657
## iter  60 value 447.972654
## iter  60 value 447.972654
## final  value 447.972654 
## converged
## trying - SEX_TEXT 
## # weights:  84 (54 variable)
## initial  value 716.295212 
## iter  10 value 463.994226
## iter  20 value 453.708227
## iter  30 value 451.022245
## iter  40 value 450.600171
## iter  50 value 450.456552
## final  value 450.453910 
## converged
## trying - EDUCATION_2_TEXT 
## # weights:  87 (56 variable)
## initial  value 716.295212 
## iter  10 value 464.349983
## iter  20 value 452.621073
## iter  30 value 449.748916
## iter  40 value 449.243133
## iter  50 value 449.064649
## iter  60 value 449.063044
## iter  60 value 449.063040
## iter  60 value 449.063040
## final  value 449.063040 
## converged
## trying - DOB_AGE_BRACKET 
## # weights:  87 (56 variable)
## initial  value 716.295212 
## iter  10 value 459.170523
## iter  20 value 450.172555
## iter  30 value 448.409313
## iter  40 value 448.115124
## iter  50 value 448.009338
## final  value 448.008688 
## converged
## trying - neuroticism_qual 
## # weights:  87 (56 variable)
## initial  value 716.295212 
## iter  10 value 463.735184
## iter  20 value 452.902780
## iter  30 value 450.779994
## iter  40 value 450.344219
## iter  50 value 450.241142
## final  value 450.239877 
## converged
## trying - extraversion_qual 
## # weights:  87 (56 variable)
## initial  value 716.295212 
## iter  10 value 461.369512
## iter  20 value 451.551182
## iter  30 value 449.765707
## iter  40 value 449.456948
## iter  50 value 449.312706
## final  value 449.311605 
## converged
## trying - openness_qual 
## # weights:  87 (56 variable)
## initial  value 716.295212 
## iter  10 value 474.441827
## iter  20 value 460.569784
## iter  30 value 458.684048
## iter  40 value 458.260373
## iter  50 value 458.071146
## iter  60 value 458.068207
## iter  60 value 458.068203
## iter  60 value 458.068203
## final  value 458.068203 
## converged
## trying - conscientiousness_qual 
## # weights:  87 (56 variable)
## initial  value 716.295212 
## iter  10 value 462.565089
## iter  20 value 450.285428
## iter  30 value 448.374425
## iter  40 value 448.041717
## iter  50 value 447.940450
## final  value 447.939717 
## converged
## trying - agreeableness_qual 
## # weights:  87 (56 variable)
## initial  value 716.295212 
## iter  10 value 471.130934
## iter  20 value 460.062578
## iter  30 value 458.306717
## iter  40 value 457.932446
## iter  50 value 457.827799
## final  value 457.826508 
## converged
##                          Df       AIC
## - CONTINENT_BORN_TEXT_1  42  998.6936
## - conscientiousness_qual 56 1007.8794
## - DOB_AGE_BRACKET        56 1008.0174
## - SEX_TEXT               54 1008.9078
## - EDUCATION_2_TEXT       56 1010.1261
## - extraversion_qual      56 1010.6232
## - HAS_LIVED_USA          58 1011.9453
## - neuroticism_qual       56 1012.4798
## <none>                   60 1013.4345
## - agreeableness_qual     56 1027.6530
## - openness_qual          56 1028.1364
## - EXPGRP_TEXT            56 1034.9043
## # weights:  66 (42 variable)
## initial  value 716.295212 
## iter  10 value 476.303301
## iter  20 value 458.769836
## iter  30 value 457.522823
## iter  40 value 457.361151
## iter  50 value 457.346844
## final  value 457.346788 
## converged
## 
## Step:  AIC=998.69
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + SEX_TEXT + EDUCATION_2_TEXT + 
##     DOB_AGE_BRACKET + neuroticism_qual + extraversion_qual + 
##     openness_qual + conscientiousness_qual + agreeableness_qual
## 
## trying - EXPGRP_TEXT 
## # weights:  60 (38 variable)
## initial  value 716.295212 
## iter  10 value 482.006351
## iter  20 value 474.009622
## iter  30 value 473.356680
## iter  40 value 473.223026
## iter  50 value 473.217076
## final  value 473.217067 
## converged
## trying - HAS_LIVED_USA 
## # weights:  63 (40 variable)
## initial  value 716.295212 
## iter  10 value 469.183596
## iter  20 value 459.957524
## iter  30 value 459.581164
## iter  40 value 459.438291
## iter  50 value 459.432258
## final  value 459.432248 
## converged
## trying - SEX_TEXT 
## # weights:  57 (36 variable)
## initial  value 716.295212 
## iter  10 value 473.679923
## iter  20 value 461.845410
## iter  30 value 461.558145
## final  value 461.555558 
## converged
## trying - EDUCATION_2_TEXT 
## # weights:  60 (38 variable)
## initial  value 716.295212 
## iter  10 value 473.796676
## iter  20 value 460.332798
## iter  30 value 459.729631
## iter  40 value 459.593057
## iter  50 value 459.590678
## iter  50 value 459.590675
## iter  50 value 459.590675
## final  value 459.590675 
## converged
## trying - DOB_AGE_BRACKET 
## # weights:  60 (38 variable)
## initial  value 716.295212 
## iter  10 value 466.226151
## iter  20 value 459.145396
## iter  30 value 458.695891
## iter  40 value 458.577337
## final  value 458.576013 
## converged
## trying - neuroticism_qual 
## # weights:  60 (38 variable)
## initial  value 716.295212 
## iter  10 value 473.522075
## iter  20 value 461.335725
## iter  30 value 460.831027
## iter  40 value 460.705076
## iter  50 value 460.702321
## final  value 460.702315 
## converged
## trying - extraversion_qual 
## # weights:  60 (38 variable)
## initial  value 716.295212 
## iter  10 value 469.906274
## iter  20 value 460.960143
## iter  30 value 460.370533
## iter  40 value 460.229655
## iter  50 value 460.225499
## iter  50 value 460.225495
## iter  50 value 460.225495
## final  value 460.225495 
## converged
## trying - openness_qual 
## # weights:  60 (38 variable)
## initial  value 716.295212 
## iter  10 value 479.863015
## iter  20 value 468.860657
## iter  30 value 468.492499
## iter  40 value 468.363461
## iter  50 value 468.359634
## iter  50 value 468.359630
## iter  50 value 468.359630
## final  value 468.359630 
## converged
## trying - conscientiousness_qual 
## # weights:  60 (38 variable)
## initial  value 716.295212 
## iter  10 value 468.075782
## iter  20 value 459.221133
## iter  30 value 458.604919
## iter  40 value 458.482645
## final  value 458.480557 
## converged
## trying - agreeableness_qual 
## # weights:  60 (38 variable)
## initial  value 716.295212 
## iter  10 value 475.102411
## iter  20 value 468.631555
## iter  30 value 468.284388
## iter  40 value 468.176880
## final  value 468.173391 
## converged
##                          Df       AIC
## - conscientiousness_qual 38  992.9611
## - DOB_AGE_BRACKET        38  993.1520
## - SEX_TEXT               36  995.1111
## - EDUCATION_2_TEXT       38  995.1813
## - extraversion_qual      38  996.4510
## - neuroticism_qual       38  997.4046
## <none>                   42  998.6936
## - HAS_LIVED_USA          40  998.8645
## - agreeableness_qual     38 1012.3468
## - openness_qual          38 1012.7193
## - EXPGRP_TEXT            38 1022.4341
## # weights:  60 (38 variable)
## initial  value 716.295212 
## iter  10 value 468.075782
## iter  20 value 459.221133
## iter  30 value 458.604919
## iter  40 value 458.482645
## final  value 458.480557 
## converged
## 
## Step:  AIC=992.96
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + SEX_TEXT + EDUCATION_2_TEXT + 
##     DOB_AGE_BRACKET + neuroticism_qual + extraversion_qual + 
##     openness_qual + agreeableness_qual
## 
## trying - EXPGRP_TEXT 
## # weights:  54 (34 variable)
## initial  value 716.295212 
## iter  10 value 480.713401
## iter  20 value 475.574541
## iter  30 value 475.215263
## iter  40 value 475.117600
## final  value 475.116554 
## converged
## trying - HAS_LIVED_USA 
## # weights:  57 (36 variable)
## initial  value 716.295212 
## iter  10 value 467.414353
## iter  20 value 461.171583
## iter  30 value 460.870733
## iter  40 value 460.800107
## final  value 460.799382 
## converged
## trying - SEX_TEXT 
## # weights:  51 (32 variable)
## initial  value 716.295212 
## iter  10 value 469.313577
## iter  20 value 462.959865
## iter  30 value 462.867221
## final  value 462.866384 
## converged
## trying - EDUCATION_2_TEXT 
## # weights:  54 (34 variable)
## initial  value 716.295212 
## iter  10 value 467.076000
## iter  20 value 460.974808
## iter  30 value 460.611621
## iter  40 value 460.555945
## final  value 460.555680 
## converged
## trying - DOB_AGE_BRACKET 
## # weights:  54 (34 variable)
## initial  value 716.295212 
## iter  10 value 466.950035
## iter  20 value 460.136328
## iter  30 value 459.802518
## iter  40 value 459.742465
## final  value 459.742009 
## converged
## trying - neuroticism_qual 
## # weights:  54 (34 variable)
## initial  value 716.295212 
## iter  10 value 468.436171
## iter  20 value 462.311018
## iter  30 value 461.959446
## iter  40 value 461.905995
## final  value 461.905669 
## converged
## trying - extraversion_qual 
## # weights:  54 (34 variable)
## initial  value 716.295212 
## iter  10 value 467.590649
## iter  20 value 462.070815
## iter  30 value 461.706140
## iter  40 value 461.629205
## final  value 461.628842 
## converged
## trying - openness_qual 
## # weights:  54 (34 variable)
## initial  value 716.295212 
## iter  10 value 475.750647
## iter  20 value 469.702561
## iter  30 value 469.386659
## iter  40 value 469.285657
## final  value 469.284755 
## converged
## trying - agreeableness_qual 
## # weights:  54 (34 variable)
## initial  value 716.295212 
## iter  10 value 474.373455
## iter  20 value 469.811080
## iter  30 value 469.517946
## iter  40 value 469.454180
## final  value 469.453810 
## converged
##                      Df       AIC
## - DOB_AGE_BRACKET    34  987.4840
## - EDUCATION_2_TEXT   34  989.1114
## - SEX_TEXT           32  989.7328
## - extraversion_qual  34  991.2577
## - neuroticism_qual   34  991.8113
## <none>               38  992.9611
## - HAS_LIVED_USA      36  993.5988
## - openness_qual      34 1006.5695
## - agreeableness_qual 34 1006.9076
## - EXPGRP_TEXT        34 1018.2331
## # weights:  54 (34 variable)
## initial  value 716.295212 
## iter  10 value 466.950035
## iter  20 value 460.136328
## iter  30 value 459.802518
## iter  40 value 459.742465
## final  value 459.742009 
## converged
## 
## Step:  AIC=987.48
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + SEX_TEXT + EDUCATION_2_TEXT + 
##     neuroticism_qual + extraversion_qual + openness_qual + agreeableness_qual
## 
## trying - EXPGRP_TEXT 
## # weights:  48 (30 variable)
## initial  value 716.295212 
## iter  10 value 480.866753
## iter  20 value 477.922202
## iter  30 value 477.654741
## iter  40 value 477.627859
## final  value 477.627793 
## converged
## trying - HAS_LIVED_USA 
## # weights:  51 (32 variable)
## initial  value 716.295212 
## iter  10 value 468.175248
## iter  20 value 462.558182
## iter  30 value 462.297855
## iter  40 value 462.254846
## final  value 462.254730 
## converged
## trying - SEX_TEXT 
## # weights:  45 (28 variable)
## initial  value 716.295212 
## iter  10 value 470.231811
## iter  20 value 463.989649
## iter  30 value 463.947544
## final  value 463.947516 
## converged
## trying - EDUCATION_2_TEXT 
## # weights:  48 (30 variable)
## initial  value 716.295212 
## iter  10 value 467.008671
## iter  20 value 462.452630
## iter  30 value 462.129610
## iter  40 value 462.118337
## final  value 462.118296 
## converged
## trying - neuroticism_qual 
## # weights:  48 (30 variable)
## initial  value 716.295212 
## iter  10 value 468.835352
## iter  20 value 463.787350
## iter  30 value 463.486672
## iter  40 value 463.476190
## final  value 463.476157 
## converged
## trying - extraversion_qual 
## # weights:  48 (30 variable)
## initial  value 716.295212 
## iter  10 value 468.344434
## iter  20 value 463.223946
## iter  30 value 462.894540
## iter  40 value 462.866913
## final  value 462.866844 
## converged
## trying - openness_qual 
## # weights:  48 (30 variable)
## initial  value 716.295212 
## iter  10 value 476.106103
## iter  20 value 470.788041
## iter  30 value 470.498705
## iter  40 value 470.459428
## final  value 470.459334 
## converged
## trying - agreeableness_qual 
## # weights:  48 (30 variable)
## initial  value 716.295212 
## iter  10 value 477.094706
## iter  20 value 470.971046
## iter  30 value 470.684279
## iter  40 value 470.650118
## final  value 470.650076 
## converged
##                      Df       AIC
## - SEX_TEXT           28  983.8950
## - EDUCATION_2_TEXT   30  984.2366
## - extraversion_qual  30  985.7337
## - neuroticism_qual   30  986.9523
## <none>               34  987.4840
## - HAS_LIVED_USA      32  988.5095
## - openness_qual      30 1000.9187
## - agreeableness_qual 30 1001.3002
## - EXPGRP_TEXT        30 1015.2556
## # weights:  45 (28 variable)
## initial  value 716.295212 
## iter  10 value 470.231811
## iter  20 value 463.989649
## iter  30 value 463.947544
## final  value 463.947516 
## converged
## 
## Step:  AIC=983.9
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + EDUCATION_2_TEXT + 
##     neuroticism_qual + extraversion_qual + openness_qual + agreeableness_qual
## 
## trying - EXPGRP_TEXT 
## # weights:  39 (24 variable)
## initial  value 716.295212 
## iter  10 value 485.022400
## iter  20 value 482.589677
## final  value 482.570082 
## converged
## trying - HAS_LIVED_USA 
## # weights:  42 (26 variable)
## initial  value 716.295212 
## iter  10 value 473.972602
## iter  20 value 466.540777
## iter  30 value 466.486440
## iter  30 value 466.486438
## iter  30 value 466.486438
## final  value 466.486438 
## converged
## trying - EDUCATION_2_TEXT 
## # weights:  39 (24 variable)
## initial  value 716.295212 
## iter  10 value 471.321804
## iter  20 value 467.003348
## final  value 466.990444 
## converged
## trying - neuroticism_qual 
## # weights:  39 (24 variable)
## initial  value 716.295212 
## iter  10 value 472.893712
## iter  20 value 468.651939
## final  value 468.633049 
## converged
## trying - extraversion_qual 
## # weights:  39 (24 variable)
## initial  value 716.295212 
## iter  10 value 475.366755
## iter  20 value 467.669826
## final  value 467.635714 
## converged
## trying - openness_qual 
## # weights:  39 (24 variable)
## initial  value 716.295212 
## iter  10 value 479.544315
## iter  20 value 474.993304
## final  value 474.952664 
## converged
## trying - agreeableness_qual 
## # weights:  39 (24 variable)
## initial  value 716.295212 
## iter  10 value 482.410024
## iter  20 value 476.468661
## final  value 476.446333 
## converged
##                      Df       AIC
## - EDUCATION_2_TEXT   24  981.9809
## - extraversion_qual  24  983.2714
## <none>               28  983.8950
## - HAS_LIVED_USA      26  984.9729
## - neuroticism_qual   24  985.2661
## - openness_qual      24  997.9053
## - agreeableness_qual 24 1000.8927
## - EXPGRP_TEXT        24 1013.1402
## # weights:  39 (24 variable)
## initial  value 716.295212 
## iter  10 value 471.321804
## iter  20 value 467.003348
## final  value 466.990444 
## converged
## 
## Step:  AIC=981.98
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + neuroticism_qual + 
##     extraversion_qual + openness_qual + agreeableness_qual
## 
## trying - EXPGRP_TEXT 
## # weights:  33 (20 variable)
## initial  value 716.295212 
## iter  10 value 489.150703
## iter  20 value 487.391804
## final  value 487.383516 
## converged
## trying - HAS_LIVED_USA 
## # weights:  36 (22 variable)
## initial  value 716.295212 
## iter  10 value 477.377240
## iter  20 value 469.976309
## final  value 469.943756 
## converged
## trying - neuroticism_qual 
## # weights:  33 (20 variable)
## initial  value 716.295212 
## iter  10 value 474.911609
## iter  20 value 471.470110
## final  value 471.465553 
## converged
## trying - extraversion_qual 
## # weights:  33 (20 variable)
## initial  value 716.295212 
## iter  10 value 476.402111
## iter  20 value 470.388840
## final  value 470.381790 
## converged
## trying - openness_qual 
## # weights:  33 (20 variable)
## initial  value 716.295212 
## iter  10 value 482.098263
## iter  20 value 478.371906
## final  value 478.366797 
## converged
## trying - agreeableness_qual 
## # weights:  33 (20 variable)
## initial  value 716.295212 
## iter  10 value 485.621098
## iter  20 value 480.607089
## final  value 480.602495 
## converged
##                      Df       AIC
## - extraversion_qual  20  980.7636
## <none>               24  981.9809
## - neuroticism_qual   20  982.9311
## - HAS_LIVED_USA      22  983.8875
## - openness_qual      20  996.7336
## - agreeableness_qual 20 1001.2050
## - EXPGRP_TEXT        20 1014.7670
## # weights:  33 (20 variable)
## initial  value 716.295212 
## iter  10 value 476.402111
## iter  20 value 470.388840
## final  value 470.381790 
## converged
## 
## Step:  AIC=980.76
## covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + neuroticism_qual + 
##     openness_qual + agreeableness_qual
## 
## trying - EXPGRP_TEXT 
## # weights:  27 (16 variable)
## initial  value 716.295212 
## iter  10 value 492.680659
## iter  20 value 489.432376
## final  value 489.432340 
## converged
## trying - HAS_LIVED_USA 
## # weights:  30 (18 variable)
## initial  value 716.295212 
## iter  10 value 482.915879
## iter  20 value 473.412742
## final  value 473.410453 
## converged
## trying - neuroticism_qual 
## # weights:  27 (16 variable)
## initial  value 716.295212 
## iter  10 value 478.676191
## iter  20 value 475.142942
## final  value 475.142866 
## converged
## trying - openness_qual 
## # weights:  27 (16 variable)
## initial  value 716.295212 
## iter  10 value 487.526012
## iter  20 value 482.434037
## final  value 482.433840 
## converged
## trying - agreeableness_qual 
## # weights:  27 (16 variable)
## initial  value 716.295212 
## iter  10 value 491.485512
## iter  20 value 484.121239
## final  value 484.121220 
## converged
##                      Df       AIC
## <none>               20  980.7636
## - neuroticism_qual   16  982.2857
## - HAS_LIVED_USA      18  982.8209
## - openness_qual      16  996.8677
## - agreeableness_qual 16 1000.2424
## - EXPGRP_TEXT        16 1010.8647
## Call:
## multinom(formula = covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + 
##     neuroticism_qual + openness_qual + agreeableness_qual, data = df_covmis2)
## 
## Coefficients:
##   (Intercept) EXPGRP_TEXTNon-Chinese Asian EXPGRP_TEXTWhite HAS_LIVED_USATRUE
## 1    1.549863                   -1.3472769       -0.9471874         0.4436547
## 3   -2.276530                    0.2021828        0.9919668         0.6868963
##   neuroticism_qualmiddle neuroticism_qualhigh openness_qualmiddle
## 1              0.8052479            0.8908038          -0.4240645
## 3              0.7455313            0.2827003          -0.7400050
##   openness_qualhigh agreeableness_qualmiddle agreeableness_qualhigh
## 1         0.5493604               -1.0754982              0.2634294
## 3        -0.6786259               -0.0199739              0.6409300
## 
## Residual Deviance: 940.7636 
## AIC: 980.7636
regm <- multinom(covqual_class ~ EXPGRP_TEXT + HAS_LIVED_USA + 
    SEX_TEXT + openness_qual + agreeableness_qual, 
           data = df_covmis2)
## # weights:  36 (22 variable)
## initial  value 716.295212 
## iter  10 value 475.607172
## iter  20 value 468.565066
## iter  30 value 468.414007
## final  value 468.413496 
## converged
stargazer::stargazer(regm)
## 
## % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com
## % Date and time: Tue, Jun 07, 2022 - 15:13:14
## \begin{table}[!htbp] \centering 
##   \caption{} 
##   \label{} 
## \begin{tabular}{@{\extracolsep{5pt}}lcc} 
## \\[-1.8ex]\hline 
## \hline \\[-1.8ex] 
##  & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ 
## \cline{2-3} 
## \\[-1.8ex] & 1 & 3 \\ 
## \\[-1.8ex] & (1) & (2)\\ 
## \hline \\[-1.8ex] 
##  EXPGRP\_TEXTNon-Chinese Asian & $-$1.388$^{**}$ & 0.168 \\ 
##   & (0.624) & (1.177) \\ 
##   & & \\ 
##  EXPGRP\_TEXTWhite & $-$0.922$^{***}$ & 0.983$^{**}$ \\ 
##   & (0.231) & (0.487) \\ 
##   & & \\ 
##  HAS\_LIVED\_USA & 0.398$^{*}$ & 0.651$^{*}$ \\ 
##   & (0.209) & (0.354) \\ 
##   & & \\ 
##  SEX\_TEXTMale & $-$0.650$^{***}$ & $-$0.377 \\ 
##   & (0.201) & (0.333) \\ 
##   & & \\ 
##  SEX\_TEXTOther & 13.803$^{***}$ & $-$2.293$^{***}$ \\ 
##   & (0.00000) & (0.000) \\ 
##   & & \\ 
##  SEX\_TEXTTransgender & 0.059 & $-$12.092$^{***}$ \\ 
##   & (1.181) & (0.00000) \\ 
##   & & \\ 
##  openness\_qualmiddle & $-$0.446 & $-$0.744 \\ 
##   & (0.470) & (0.671) \\ 
##   & & \\ 
##  openness\_qualhigh & 0.534 & $-$0.700 \\ 
##   & (0.497) & (0.725) \\ 
##   & & \\ 
##  agreeableness\_qualmiddle & $-$1.272$^{**}$ & 0.069 \\ 
##   & (0.583) & (1.151) \\ 
##   & & \\ 
##  agreeableness\_qualhigh & $-$0.315 & 0.501 \\ 
##   & (0.630) & (1.209) \\ 
##   & & \\ 
##  Constant & 2.865$^{***}$ & $-$1.569 \\ 
##   & (0.738) & (1.334) \\ 
##   & & \\ 
## \hline \\[-1.8ex] 
## Akaike Inf. Crit. & 980.827 & 980.827 \\ 
## \hline 
## \hline \\[-1.8ex] 
## \textit{Note:}  & \multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ 
## \end{tabular} 
## \end{table}
ggcoef_multinom(regm, exponentiate = TRUE)

Let’s reduce the size of our data

df_covmis$EDUCATION_2_TEXT <- fct_recode(df_covmis$EDUCATION_1 %>% as.character,
          "No college degree"="1",
          "No college degree"="2",
          "No college degree"="3",
          "College degree"="4",
          "College degree"="5",
          "Graduate degree"="6",
          "Graduate degree"="7",
          "Graduate degree"="8")

df_covmis$DOB_AGE_BRACKET <- fct_recode(df_covmis$DOB_YEAR_PERIODE %>% as.character,
          "-25y"="(1995,2005]",
          "25 - 45y"="(1985,1995]",
          "25 - 45y"="(1975,1985]",
          "+45y"="(1944,1955]", 
          "+45y"="(1955,1965]",
          "+45y"="(1965,1975]") %>%
  relevel("-25y")

df_covmis$HH_INCOME_TEXT2 <- fct_recode(df$HH_INCOME %>% as.character,
"Less than $30,000"="1",
"Less than $30,000"="2",
"$30,000 to $70,000"="3",
"$30,000 to $70,000"="4",
"$70,000 or more"="5",
"$70,000 or more"="6",
"$70,000 or more"="7",
"$70,000 or more"="8")

df_covmis$covqual_class <- relevel(df_covmis$covqual_class, "1")


df_covmis3 <- df_covmis %>%
                   filter(EXPGRP_TEXT != "Non-Chinese Asian" &
                            !(CONTINENT_BORN_TEXT_1 %in% c("4Tigers and Japan", "Africa", "Middle East",
                                                           "North America", "Oceania", "South America")) &
                            !(is.na(CONTINENT_BORN_TEXT_1)) &
                            !(SEX_TEXT %in% c("Other", "Transgender")) &
                            HH_INCOME_TEXT != "$500,000 or more")

df_covmis3$CONTINENT_BORN_TEXT_1 <- df_covmis3$CONTINENT_BORN_TEXT_1 %>% as.character %>% as.factor %>% relevel("USA")

regm <- multinom(covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + DOB_AGE_BRACKET + SEX_TEXT + EDUCATION_2_TEXT + HH_INCOME_TEXT2 + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, 
                 data = df_covmis3)
## # weights:  69 (44 variable)
## initial  value 653.674312 
## iter  10 value 460.900377
## iter  20 value 412.307293
## iter  30 value 407.613770
## iter  40 value 407.525232
## final  value 407.524572 
## converged
summary(regm)
## Call:
## multinom(formula = covqual_class ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + 
##     DOB_AGE_BRACKET + SEX_TEXT + EDUCATION_2_TEXT + HH_INCOME_TEXT2 + 
##     neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + 
##     agreeableness_qual, data = df_covmis3)
## 
## Coefficients:
##   (Intercept) EXPGRP_TEXTWhite CONTINENT_BORN_TEXT_1Central Eastern Europe
## 2   -2.447998        0.8705434                                  0.48914147
## 3   -4.280277        2.6819800                                  0.01464403
##   CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Western Europe
## 2                           -0.05794654                          -0.2793198
## 3                            0.92692798                          -1.1806136
##   DOB_AGE_BRACKET+45y DOB_AGE_BRACKET25 - 45y SEX_TEXTMale
## 2          0.09266112             0.003424176    0.6742221
## 3         -0.68463859             0.115706962   -0.1579060
##   EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 2                     -0.3176707                      -0.2596761
## 3                     -0.2561158                      -1.2901992
##   HH_INCOME_TEXT2$30,000 to $70,000 HH_INCOME_TEXT2$70,000 or more
## 2                        -0.1264863                     -0.6275616
## 3                        -0.1333308                     -0.4464852
##   neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 2             -0.9303059           -0.7018127               0.3483925
## 3             -0.1047777           -0.3943623               0.5141155
##   extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 2           -0.04922208           0.6587268        -0.5093904
## 3            0.60811448          -0.5808039        -1.5987915
##   conscientiousness_qualmiddle conscientiousness_qualhigh
## 2                    0.1704969                  0.2422436
## 3                    0.4946263                  1.1397106
##   agreeableness_qualmiddle agreeableness_qualhigh
## 2                1.1897790              0.0910851
## 3                0.8669669              0.1789035
## 
## Std. Errors:
##   (Intercept) EXPGRP_TEXTWhite CONTINENT_BORN_TEXT_1Central Eastern Europe
## 2   0.9800527        0.3272734                                   0.3730280
## 3   1.6409939        0.7759154                                   0.5533785
##   CONTINENT_BORN_TEXT_1Developping Asia CONTINENT_BORN_TEXT_1Western Europe
## 2                             0.4699871                           0.3234785
## 3                             1.0337213                           0.5334005
##   DOB_AGE_BRACKET+45y DOB_AGE_BRACKET25 - 45y SEX_TEXTMale
## 2           0.4202992               0.2708421    0.2383787
## 3           0.6631363               0.4049085    0.3683903
##   EDUCATION_2_TEXTCollege degree EDUCATION_2_TEXTGraduate degree
## 2                      0.2646574                       0.3511567
## 3                      0.3884076                       0.6344856
##   HH_INCOME_TEXT2$30,000 to $70,000 HH_INCOME_TEXT2$70,000 or more
## 2                         0.2651938                      0.3041295
## 3                         0.4110522                      0.4722572
##   neuroticism_qualmiddle neuroticism_qualhigh extraversion_qualmiddle
## 2              0.3633538            0.4323644               0.3134390
## 3              0.5332415            0.6700849               0.4919758
##   extraversion_qualhigh openness_qualmiddle openness_qualhigh
## 2             0.4569988           0.5165668         0.5477978
## 3             0.6621812           0.6594343         0.7113076
##   conscientiousness_qualmiddle conscientiousness_qualhigh
## 2                    0.3557977                  0.4392429
## 3                    0.6302087                  0.6898526
##   agreeableness_qualmiddle agreeableness_qualhigh
## 2                 0.629212              0.7085308
## 3                 1.119453              1.2072278
## 
## Residual Deviance: 815.0491 
## AIC: 903.0491
table(df_covmis$EDUCATION_2_TEXT, df_covmis$EXPGRP_TEXT) %>% lprop() %>% xtable::xtable()
## % latex table generated in R 4.2.0 by xtable 1.8-4 package
## % Tue Jun  7 15:13:17 2022
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrrrr}
##   \hline
##  & Chinese & Non-Chinese Asian & White & Total \\ 
##   \hline
## No college degree & 27.17 & 0.72 & 72.10 & 100.00 \\ 
##   College degree & 36.19 & 2.72 & 61.09 & 100.00 \\ 
##   Graduate degree & 44.19 & 4.65 & 51.16 & 100.00 \\ 
##   All & 33.99 & 2.27 & 63.75 & 100.00 \\ 
##    \hline
## \end{tabular}
## \end{table}
ggcoef_multinom(
  regm,
  exponentiate = TRUE
)

Create a quantitative variable of covid skeptism

options: - Sem scoring on latent variable - Coordinate on the first variable of MFA which resume almost 50% of the variance - Standardization of score

SEM Scoring for covid skepticism

library(questionr)
library(FactoMineR)
library(tidyverse)
library(lavaan)
## This is lavaan 0.6-11
## lavaan is FREE software! Please report any bugs.
## 
## Attaching package: 'lavaan'
## The following object is masked from 'package:psych':
## 
##     cor2cov
library(semTools)
## 
## ###############################################################################
## This is semTools 0.5-6
## All users of R (or SEM) are invited to submit functions or ideas for functions.
## ###############################################################################
## 
## Attaching package: 'semTools'
## The following objects are masked from 'package:psych':
## 
##     reliability, skew
## The following object is masked from 'package:readr':
## 
##     clipboard
library(lavaanPlot)
library(MVN)
library(MIIVsem)
## This is MIIVsem 0.5.8
## MIIVsem is BETA software! Please report any bugs.
options(max.print=2000)

’ Attention =~ covmis_att_flu + covmis_att_afrDie + covmis_att_eldrNoBgDl + covmis_att_rareNoWorr + covmis_att_bgThrt Conspiracy =~ covmis_cnsp_ctiusAsian + covmis_cnsp_stpCovStpImmi + covmis_cnsp_redIntWthChina + covmis_cnsp_chnsCovRcst Origin =~ covmis_orgn_covPlnnd + covmis_orgn_covNat + covmis_orgn_covNgeenLab + covmis_orgn_scntFkNwsCov Politics =~ covmis_pltc_polBgDlIntrst + covmis_pltc_covNtSerPolSay + covmis_pltc_polDwnplCovPlpLDngr Coverage =~ covmis_cvrg_mdiaCovBgrDl + covmis_cvrg_nwsGdJbComCov + covmis_cvrg_mdiaUseCovMkTrmpRepLkBd AntiVacc =~ covmis_anti_frGovUseCovMndtVacc + covmis_anti_thnksNoCovVacc + covmis_anti_covVacEffRedVirus MedSkep =~ covmis_mdsk_medOrgUntrust + covmis_mdsk_skeptInfoDocSci + covmis_mdsk_medOrgRecBstInt + covmis_mdsk_fllwRecMedOrgImp Attention ~~ Conspiracy Attention ~~ Origin Attention ~~ Politics Attention ~~ Coverage Attention ~~ AntiVacc Attention ~~ MedSkep Conspiracy ~~ Origin Conspiracy ~~ Politics Conspiracy ~~ Coverage Conspiracy ~~ AntiVacc Conspiracy ~~ MedSkep Origin ~~ Politics Origin ~~ Coverage Origin ~~ AntiVacc Origin ~~ MedSkep Politics ~~ Coverage Politics ~~ AntiVacc Politics ~~ MedSkep Coverage ~~ AntiVacc Coverage ~~ MedSkep AntiVacc ~~ MedSkep CovidSkepticism =~ Attention + Conspiracy + Origin + Politics + Coverage + AntiVacc + MedSkep ’

model <- '
CovidSkepticism =~ covmis_att_flu + covmis_att_afrDie + covmis_att_eldrNoBgDl + covmis_att_rareNoWorr + covmis_att_bgThrt + covmis_cnsp_ctiusAsian + covmis_cnsp_stpCovStpImmi + covmis_cnsp_redIntWthChina + covmis_cnsp_chnsCovRcst + covmis_orgn_covPlnnd + covmis_orgn_covNat + covmis_orgn_covNgeenLab + covmis_orgn_scntFkNwsCov + covmis_pltc_polBgDlIntrst + covmis_pltc_covNtSerPolSay + covmis_pltc_polDwnplCovPlpLDngr + covmis_cvrg_mdiaCovBgrDl + covmis_cvrg_nwsGdJbComCov + covmis_cvrg_mdiaUseCovMkTrmpRepLkBd + covmis_anti_frGovUseCovMndtVacc + covmis_anti_thnksNoCovVacc + covmis_anti_covVacEffRedVirus + covmis_mdsk_medOrgUntrust + covmis_mdsk_skeptInfoDocSci + covmis_mdsk_medOrgRecBstInt + covmis_mdsk_fllwRecMedOrgImp
'
fit <- cfa(model, data = df_covmis, estimator = "ML")
semPlot::semPaths(fit)

summary(fit, fit.measures=T, standardized=T, rsquare=T) 
## lavaan 0.6-11 ended normally after 36 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        52
##                                                       
##   Number of observations                           662
##                                                       
## Model Test User Model:
##                                                       
##   Test statistic                              3064.483
##   Degrees of freedom                               299
##   P-value (Chi-square)                           0.000
## 
## Model Test Baseline Model:
## 
##   Test statistic                              9296.673
##   Degrees of freedom                               325
##   P-value                                        0.000
## 
## User Model versus Baseline Model:
## 
##   Comparative Fit Index (CFI)                    0.692
##   Tucker-Lewis Index (TLI)                       0.665
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -26184.534
##   Loglikelihood unrestricted model (H1)     -24652.292
##                                                       
##   Akaike (AIC)                               52473.067
##   Bayesian (BIC)                             52706.821
##   Sample-size adjusted Bayesian (BIC)        52541.719
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.118
##   90 Percent confidence interval - lower         0.114
##   90 Percent confidence interval - upper         0.122
##   P-value RMSEA <= 0.05                          0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.084
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Expected
##   Information saturated (h1) model          Structured
## 
## Latent Variables:
##                      Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   CovidSkepticism =~                                                      
##     covmis_att_flu      1.000                               0.573    0.378
##     covmis_att_frD      0.685    0.124    5.527    0.000    0.393    0.254
##     cvms_tt_ldrNBD      0.876    0.107    8.185    0.000    0.502    0.480
##     cvms_tt_rrNWrr      0.994    0.113    8.790    0.000    0.570    0.569
##     cvms_tt_bgThrt      0.943    0.132    7.133    0.000    0.541    0.370
##     cvms_cnsp_ctsA      0.525    0.085    6.158    0.000    0.301    0.295
##     cvms_cnsp_sCSI      0.951    0.132    7.213    0.000    0.545    0.377
##     cvms_cnsp_rIWC      1.169    0.145    8.039    0.000    0.670    0.462
##     cvms_cnsp_chCR      1.426    0.175    8.166    0.000    0.818    0.478
##     cvms_rgn_cvPln      1.482    0.155    9.543    0.000    0.850    0.732
##     covms_rgn_cvNt      1.413    0.160    8.841    0.000    0.810    0.577
##     cvms_rgn_cvNgL      1.711    0.182    9.398    0.000    0.981    0.694
##     cvms_rgn_scFNC      1.743    0.182    9.553    0.000    0.999    0.735
##     cvms_pltc_pBDI      1.727    0.199    8.683    0.000    0.990    0.551
##     cvms_pltc_NSPS      1.764    0.184    9.562    0.000    1.012    0.737
##     cvms_plt_DCPLD      1.297    0.144    9.033    0.000    0.744    0.613
##     cvms_cvrg_mCBD      1.932    0.205    9.410    0.000    1.108    0.697
##     cvms_cvrg_GJCC      0.652    0.105    6.195    0.000    0.374    0.298
##     cvms_c_UCMTRLB      1.723    0.187    9.238    0.000    0.988    0.655
##     cvms_nt_fGUCMV      1.812    0.190    9.524    0.000    1.039    0.727
##     cvms_nt_thnNCV      1.168    0.126    9.296    0.000    0.670    0.669
##     cvms_nt_cvVERV      1.412    0.152    9.315    0.000    0.810    0.673
##     cvms_mdsk_mdOU      1.755    0.186    9.443    0.000    1.006    0.705
##     cvms_mdsk_sIDS      1.658    0.171    9.719    0.000    0.951    0.786
##     cvms_mdsk_ORBI      1.513    0.162    9.334    0.000    0.867    0.678
##     cvms_mdsk_RMOI      1.452    0.152    9.574    0.000    0.833    0.741
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .covmis_att_flu    1.970    0.109   18.017    0.000    1.970    0.857
##    .covmis_att_frD    2.232    0.123   18.120    0.000    2.232    0.935
##    .cvms_tt_ldrNBD    0.841    0.047   17.876    0.000    0.841    0.769
##    .cvms_tt_rrNWrr    0.680    0.038   17.688    0.000    0.680    0.677
##    .cvms_tt_bgThrt    1.842    0.102   18.025    0.000    1.842    0.863
##    .cvms_cnsp_ctsA    0.951    0.053   18.093    0.000    0.951    0.913
##    .cvms_cnsp_sCSI    1.791    0.099   18.018    0.000    1.791    0.858
##    .cvms_cnsp_rIWC    1.653    0.092   17.906    0.000    1.653    0.786
##    .cvms_cnsp_chCR    2.258    0.126   17.880    0.000    2.258    0.772
##    .cvms_rgn_cvPln    0.625    0.037   16.969    0.000    0.625    0.464
##    .covms_rgn_cvNt    1.314    0.074   17.665    0.000    1.314    0.667
##    .cvms_rgn_cvNgL    1.039    0.060   17.212    0.000    1.039    0.519
##    .cvms_rgn_scFNC    0.850    0.050   16.949    0.000    0.850    0.460
##    .cvms_pltc_pBDI    2.252    0.127   17.733    0.000    2.252    0.697
##    .cvms_pltc_NSPS    0.858    0.051   16.930    0.000    0.858    0.456
##    .cvms_plt_DCPLD    0.920    0.052   17.557    0.000    0.920    0.625
##    .cvms_cvrg_mCBD    1.302    0.076   17.195    0.000    1.302    0.515
##    .cvms_cvrg_GJCC    1.439    0.080   18.091    0.000    1.439    0.911
##    .cvms_c_UCMTRLB    1.296    0.075   17.396    0.000    1.296    0.570
##    .cvms_nt_fGUCMV    0.964    0.057   17.008    0.000    0.964    0.472
##    .cvms_nt_thnNCV    0.554    0.032   17.337    0.000    0.554    0.553
##    .cvms_nt_cvVERV    0.791    0.046   17.316    0.000    0.791    0.547
##    .cvms_mdsk_mdOU    1.024    0.060   17.147    0.000    1.024    0.503
##    .cvms_mdsk_sIDS    0.559    0.034   16.479    0.000    0.559    0.382
##    .cvms_mdsk_ORBI    0.886    0.051   17.295    0.000    0.886    0.541
##    .cvms_mdsk_RMOI    0.569    0.034   16.903    0.000    0.569    0.451
##     CovidSkepticsm    0.329    0.067    4.918    0.000    1.000    1.000
## 
## R-Square:
##                    Estimate
##     covmis_att_flu    0.143
##     covmis_att_frD    0.065
##     cvms_tt_ldrNBD    0.231
##     cvms_tt_rrNWrr    0.323
##     cvms_tt_bgThrt    0.137
##     cvms_cnsp_ctsA    0.087
##     cvms_cnsp_sCSI    0.142
##     cvms_cnsp_rIWC    0.214
##     cvms_cnsp_chCR    0.228
##     cvms_rgn_cvPln    0.536
##     covms_rgn_cvNt    0.333
##     cvms_rgn_cvNgL    0.481
##     cvms_rgn_scFNC    0.540
##     cvms_pltc_pBDI    0.303
##     cvms_pltc_NSPS    0.544
##     cvms_plt_DCPLD    0.375
##     cvms_cvrg_mCBD    0.485
##     cvms_cvrg_GJCC    0.089
##     cvms_c_UCMTRLB    0.430
##     cvms_nt_fGUCMV    0.528
##     cvms_nt_thnNCV    0.447
##     cvms_nt_cvVERV    0.453
##     cvms_mdsk_mdOU    0.497
##     cvms_mdsk_sIDS    0.618
##     cvms_mdsk_ORBI    0.459
##     cvms_mdsk_RMOI    0.549
fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
  filter(!(rhs%in%lhs)) %>% 
  group_by(lhs) %>%
  summarise(sest=sum(est)) %>%
  inner_join(fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
    filter((rhs%in%lhs)) %>% 
    dplyr::select(rhs,est),
  by = c("lhs" = "rhs")) %>%
  mutate(factor=sest*est) %>%
  dplyr::select(factor) %>%
  colSums()
## factor 
##      0
f_semscoring_latent <- function(fit, data, scaled = TRUE){
  
  df_sem_estimate <- fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1)
  v_latent_var <- df_sem_estimate$lhs %>% as_factor %>% levels
  
  f_calculate_latent <- function(latent_var, data2){
    
    v_obs_var <- df_sem_estimate %>% filter(lhs==latent_var) %>% dplyr::select(rhs) %>% as_vector()
    
    d <- tibble(matrix(ncol = 0, nrow = nrow(data)))
    for (obs_var in v_obs_var) {
      res <- data2[,obs_var] * (df_sem_estimate %>% filter(lhs==latent_var, rhs==obs_var) %>% dplyr::select(est) %>% as.numeric())
      d[,obs_var] <- res
    }
    res <- rowSums(d)
    if(scaled) res <- res %>% scale
    return(res)
  }
  
  d <- tibble(matrix(ncol = 0, nrow = nrow(data)))
  for(j in v_latent_var){
    data[,j] <- f_calculate_latent(j, data)
  }
  data <- data %>% dplyr::select(v_latent_var)
  return(data)
}


df_scorecovmis <- f_semscoring_latent(fit, df_covmis, scaled = F)
## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(v_latent_var)` instead of `v_latent_var` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.
(df_scorecovmis$CovidSkepticism == min(df_scorecovmis$CovidSkepticism)) %>% which()
## [1] 455 589
df_covmis %>% dplyr::select(matches("^covmis")) %>% as_tibble %>% dplyr::slice(455L,589L)
## # A tibble: 2 × 26
##   covmis_att_flu covmis_att_afrDie covmis_att_eldrNoBgDl covmis_att_rareNoWorr
##            <int>             <dbl>                 <int>                 <int>
## 1              1                 1                     1                     1
## 2              1                 1                     1                     1
## # … with 22 more variables: covmis_att_bgThrt <dbl>,
## #   covmis_cnsp_ctiusAsian <int>, covmis_cnsp_stpCovStpImmi <int>,
## #   covmis_cnsp_redIntWthChina <int>, covmis_cnsp_chnsCovRcst <dbl>,
## #   covmis_orgn_covPlnnd <int>, covmis_orgn_covNat <dbl>,
## #   covmis_orgn_covNgeenLab <int>, covmis_orgn_scntFkNwsCov <int>,
## #   covmis_pltc_polBgDlIntrst <int>, covmis_pltc_covNtSerPolSay <int>,
## #   covmis_pltc_polDwnplCovPlpLDngr <dbl>, covmis_cvrg_mdiaCovBgrDl <int>, …
standardisation_hierar <- function(x){
  x <-  (x- (fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
  filter(!(rhs%in%lhs)) %>% 
  group_by(lhs) %>%
  summarise(sest=sum(est)) %>%
  inner_join(fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
    filter((rhs%in%lhs)) %>% 
    dplyr::select(rhs,est),
  by = c("lhs" = "rhs")) %>%
  mutate(factor=sest*est) %>%
  dplyr::select(factor) %>%
  colSums()))  / (fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
  filter(!(rhs%in%lhs)) %>% 
  group_by(lhs) %>%
  summarise(sest=sum(est)) %>%
  inner_join(fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
    filter((rhs%in%lhs)) %>% 
    dplyr::select(rhs,est),
  by = c("lhs" = "rhs")) %>%
  mutate(factor=sest*est) %>%
  dplyr::select(factor) %>%
  colSums() * 6) * 100
}

standardisation <- function(x){
  x <-  (x- (fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
  dplyr::select(est) %>%
  colSums()))  / ((fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
  dplyr::select(est) %>%
  colSums()) * 6) * 100
}

df_scorecovmis$CovidSkepticism <- df_scorecovmis$CovidSkepticism %>% standardisation()
df_covmis$CovidSkepticism <- df_scorecovmis$CovidSkepticism
df_covmis$CovidSkepticism %>% summary
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   8.982  17.231  20.404  29.763  75.678
df_covmis$CovidSkepticism %>% hist()

write.csv(df_covmis,"./data/mjolnir_clean_v6_Covmis.csv")

cleaning data

Some group of participants are not big enough to be analyzed and produces noises we want to avoid.

df_covmis$HH_INCOME_TEXT2 <- fct_recode(df$HH_INCOME %>% as.character,
"Less than $30,000"="1",
"Less than $30,000"="2",
"$30,000 to $70,000"="3",
"$30,000 to $70,000"="4",
"$70,000 or more"="5",
"$70,000 or more"="6",
"$70,000 or more"="7",
"$70,000 or more"="8")
df_covmis$EDUCATION_2_TEXT <- fct_recode(df$EDUCATION_1 %>% as.character,
          "No college degree"="1",
          "No college degree"="2",
          "No college degree"="3",
          "College degree"="4",
          "College degree"="5",
          "Graduate degree"="6",
          "Graduate degree"="7",
          "Graduate degree"="8")
df_covmis$DOB_YEAR_PERIODE <- df_covmis$DOB_YEAR %>% cut(breaks = c(1944,1955,1965,1975,1985,1995,2005))
df_covmis$DOB_AGE_BRACKET <- fct_recode(df_covmis$DOB_YEAR_PERIODE %>% as.character,
          "-25y"="(1995,2005]",
          "25 - 35y"="(1985,1995]",
          "35 - 45y"="(1975,1985]",
          "+45y"="(1944,1955]", 
          "+45y"="(1955,1965]",
          "+45y"="(1965,1975]")
f_normfactor <- function(v){
  res <- AMBI::f_normalisation(v)
  res <- res*3
  res <- cut(res, c(-0.1,1,2,3), labels=c('weak','middle','high'))
  return(res)
}
df_covmis$neuroticism_qual <- f_normfactor(df_covmis$AMBI_BIG5_Neuroticism)
df_covmis$extraversion_qual <- f_normfactor(df_covmis$AMBI_BIG5_Extraversion)
df_covmis$openness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Openness)
df_covmis$conscientiousness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Conscientiousness)
df_covmis$agreeableness_qual <- f_normfactor(df_covmis$AMBI_BIG5_Agreeableness)



df_covmis2 <-  df_covmis %>%
  filter(EXPGRP_TEXT != "Non-Chinese Asian" &
           !(CONTINENT_BORN_TEXT_1 %in% c("4Tigers and Japan", "Africa", "Middle East",
                                        "North America", "Oceania", "South America")) &
           !(is.na(CONTINENT_BORN_TEXT_1)) &
           !(SEX_TEXT %in% c("Other", "Transgender")) &
           HH_INCOME_TEXT != "$500,000 or more") 

df_covmis2 %>%
  dplyr::select(EXPGRP_TEXT) %>%
  freq()
##           n    % val%
## Chinese 195 32.6 32.6
## White   404 67.4 67.4
df_covmis2 %>%
  dplyr::select(CONTINENT_BORN_TEXT_1) %>%
  freq()
##                          n    % val%
## USA                    361 60.3 60.3
## 4Tigers and Japan        0  0.0  0.0
## Africa                   0  0.0  0.0
## Central Eastern Europe  76 12.7 12.7
## Developping Asia        60 10.0 10.0
## Middle East              0  0.0  0.0
## North America            0  0.0  0.0
## Oceania                  0  0.0  0.0
## South America            0  0.0  0.0
## Western Europe         102 17.0 17.0
df_covmis2 %>%
  dplyr::select(SEX_TEXT) %>%
  freq()
##          n    % val%
## Female 334 55.8 55.8
## Male   265 44.2 44.2
df_covmis2 %>%
  dplyr::select(agreeableness_qual) %>%
  freq()
##          n    % val%
## weak    24  4.0  4.0
## middle 404 67.4 67.4
## high   171 28.5 28.5
df_covmis2 %>%
  dplyr::select(conscientiousness_qual) %>%
  freq()
##          n    % val%
## weak    76 12.7 12.7
## middle 355 59.3 59.3
## high   168 28.0 28.0
ex <- car::powerTransform(df_covmis2$CovidSkepticism+0.0000001)
df_covmis2$CovidSkepticism^ex$lambda %>% hist()

df_covmis2$CovidSkepticismnorm <-  df_covmis2$CovidSkepticism^ex$lambda
df_covmis2 %>% 
  gather(key = "Covmis_var", value="Covmis_res", df_covmis %>% colnames %>% str_detect("^covmis") %>% which) %>%
  ggplot(aes(Covmis_res, CovidSkepticism)) +
  geom_point() +
  geom_smooth(method = "loess") + 
  facet_wrap(~ Covmis_var, ncol = 5)
## `geom_smooth()` using formula 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.3405e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.3405e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.118e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.118e-16
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 0.000625
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 8.8071e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 8.8071e-16
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.7365e-30
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.7365e-30
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.5791e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.5791e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 4.3171e-30
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 4.3171e-30
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 4.5595e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 4.5595e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 5.3803e-30
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 5.3803e-30
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.0048e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 2.0048e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 5.6618e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 5.6618e-16
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.3814e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.3814e-16
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 3
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 3
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 0
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.4577e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.4577e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.2436e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.2436e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 0
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 2.1297e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 2.1297e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.2732e-15
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.2732e-15
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 7.5338e-30
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 7.5338e-30
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.2269e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.2269e-16
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.1545e-16
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 2.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.1545e-16
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.975
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 1.025
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.6576e-30
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 1
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.975
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 1.025
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.6576e-30
## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 1

res.aov <- rstatix::anova_test(CovidSkepticismnorm ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HH_INCOME_TEXT2 + SEX_TEXT + EDUCATION_2_TEXT + DOB_AGE_BRACKET + AMBI_BIG5_Neuroticism + AMBI_BIG5_Extraversion + AMBI_BIG5_Openness + AMBI_BIG5_Conscientiousness + AMBI_BIG5_Agreeableness, data = df_covmis2)
## Coefficient covariances computed by hccm()
res.aov
## ANOVA Table (type II tests)
## 
##                         Effect DFn DFd      F        p p<.05      ges
## 1                  EXPGRP_TEXT   1 581 14.677 1.41e-04     * 0.025000
## 2        CONTINENT_BORN_TEXT_1   3 581  1.176 3.18e-01       0.006000
## 3              HH_INCOME_TEXT2   2 581  0.841 4.32e-01       0.003000
## 4                     SEX_TEXT   1 581  0.504 4.78e-01       0.000866
## 5             EDUCATION_2_TEXT   2 581  4.251 1.50e-02     * 0.014000
## 6              DOB_AGE_BRACKET   3 581  1.423 2.35e-01       0.007000
## 7        AMBI_BIG5_Neuroticism   1 581  0.445 5.05e-01       0.000765
## 8       AMBI_BIG5_Extraversion   1 581 26.899 2.97e-07     * 0.044000
## 9           AMBI_BIG5_Openness   1 581 55.870 2.86e-13     * 0.088000
## 10 AMBI_BIG5_Conscientiousness   1 581  0.233 6.29e-01       0.000401
## 11     AMBI_BIG5_Agreeableness   1 581 22.109 3.22e-06     * 0.037000
res.aov <- rstatix::anova_test(CovidSkepticismnorm ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HH_INCOME_TEXT2 + SEX_TEXT + EDUCATION_2_TEXT:DOB_AGE_BRACKET, data = df_covmis2)
## Coefficient covariances computed by hccm()
## Note: model has aliased coefficients
##       sums of squares computed by model comparison
res.aov
## ANOVA Table (type II tests)
## 
##                             Effect DFn DFd      F     p p<.05      ges
## 1                      EXPGRP_TEXT   1 580  2.211 0.138       0.004000
## 2            CONTINENT_BORN_TEXT_1   3 580  3.614 0.013     * 0.018000
## 3                  HH_INCOME_TEXT2   2 580  0.103 0.902       0.000356
## 4                         SEX_TEXT   1 580 10.893 0.001     * 0.018000
## 5 EDUCATION_2_TEXT:DOB_AGE_BRACKET  11 580  2.870 0.001     * 0.052000
res.aov <- rstatix::anova_test(CovidSkepticismnorm ~ AMBI_BIG5_Neuroticism + AMBI_BIG5_Extraversion + AMBI_BIG5_Openness + AMBI_BIG5_Conscientiousness + AMBI_BIG5_Agreeableness, data = df_covmis2)
## Coefficient covariances computed by hccm()
res.aov
## ANOVA Table (type II tests)
## 
##                        Effect DFn DFd      F        p p<.05   ges
## 1       AMBI_BIG5_Neuroticism   1 593  1.154 2.83e-01       0.002
## 2      AMBI_BIG5_Extraversion   1 593 24.199 1.13e-06     * 0.039
## 3          AMBI_BIG5_Openness   1 593 43.480 9.49e-11     * 0.068
## 4 AMBI_BIG5_Conscientiousness   1 593  2.485 1.16e-01       0.004
## 5     AMBI_BIG5_Agreeableness   1 593 31.869 2.56e-08     * 0.051
pwc <- df_covmis2 %>% tukey_hsd(CovidSkepticismnorm ~ EXPGRP_TEXT + CONTINENT_BORN_TEXT_1 + HH_INCOME_TEXT2 + SEX_TEXT + EDUCATION_2_TEXT + DOB_AGE_BRACKET + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual)
pwc
## # A tibble: 35 × 9
##    term             group1 group2 null.value estimate conf.low conf.high   p.adj
##  * <chr>            <chr>  <chr>       <dbl>    <dbl>    <dbl>     <dbl>   <dbl>
##  1 EXPGRP_TEXT      Chine… White           0   0.271     0.134    0.408  1.15e-4
##  2 CONTINENT_BORN_… USA    Centr…          0   0.480     0.219    0.740  1.55e-5
##  3 CONTINENT_BORN_… USA    Devel…          0   0.0568   -0.231    0.344  9.57e-1
##  4 CONTINENT_BORN_… USA    Weste…          0   0.113    -0.118    0.344  5.9 e-1
##  5 CONTINENT_BORN_… Centr… Devel…          0  -0.423    -0.779   -0.0666 1.25e-2
##  6 CONTINENT_BORN_… Centr… Weste…          0  -0.367    -0.679   -0.0540 1.39e-2
##  7 CONTINENT_BORN_… Devel… Weste…          0   0.0562   -0.279    0.392  9.73e-1
##  8 HH_INCOME_TEXT2  Less … $30,0…          0  -0.0242   -0.211    0.163  9.5 e-1
##  9 HH_INCOME_TEXT2  Less … $70,0…          0  -0.0760   -0.265    0.113  6.12e-1
## 10 HH_INCOME_TEXT2  $30,0… $70,0…          0  -0.0518   -0.241    0.137  7.96e-1
## # … with 25 more rows, and 1 more variable: p.adj.signif <chr>
res.aov <- anova_test(CovidSkepticismnorm ~ EXPGRP_TEXT , data = df_covmis2)
## Coefficient covariances computed by hccm()
res.aov
## ANOVA Table (type II tests)
## 
##        Effect DFn DFd      F        p p<.05  ges
## 1 EXPGRP_TEXT   1 597 12.324 0.000481     * 0.02
pwc <- df_covmis2 %>% tukey_hsd(CovidSkepticismnorm ~ EXPGRP_TEXT)
pwc
## # A tibble: 1 × 9
##   term        group1  group2 null.value estimate conf.low conf.high    p.adj
## * <chr>       <chr>   <chr>       <dbl>    <dbl>    <dbl>     <dbl>    <dbl>
## 1 EXPGRP_TEXT Chinese White           0    0.271    0.119     0.423 0.000481
## # … with 1 more variable: p.adj.signif <chr>
res.aov <- rstatix::anova_test(CovidSkepticismnorm ~ demo_class + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, data = df_covmis2)
## Coefficient covariances computed by hccm()
res.aov
## ANOVA Table (type II tests)
## 
##                   Effect DFn DFd      F        p p<.05      ges
## 1             demo_class   5 583  5.792 3.13e-05     * 0.047000
## 2       neuroticism_qual   2 583  0.400 6.71e-01       0.001000
## 3      extraversion_qual   2 583  2.223 1.09e-01       0.008000
## 4          openness_qual   2 583 23.936 1.02e-10     * 0.076000
## 5 conscientiousness_qual   2 583  0.220 8.03e-01       0.000753
## 6     agreeableness_qual   2 583 11.982 7.95e-06     * 0.039000
df_covmis2 <- fastDummies::dummy_cols(df_covmis2, select_columns = c("EXPGRP_TEXT","CONTINENT_BORN_TEXT_1", 'HH_INCOME_TEXT2', "SEX_TEXT", "EDUCATION_2_TEXT", "DOB_AGE_BRACKET"))

colnames(df_covmis2) <- str_replace_all(colnames(df_covmis2), "[ ,+$-]", "")

res.aov <- anova_test(CovidSkepticismnorm ~ EXPGRP_TEXT_Chinese + EXPGRP_TEXT_White + CONTINENT_BORN_TEXT_1_CentralEasternEurope + CONTINENT_BORN_TEXT_1_DeveloppingAsia + CONTINENT_BORN_TEXT_1_USA + CONTINENT_BORN_TEXT_1_WesternEurope + HH_INCOME_TEXT2_Lessthan30000 + HH_INCOME_TEXT2_30000to70000 + HH_INCOME_TEXT2_70000ormore + SEX_TEXT_Female + SEX_TEXT_Male + EDUCATION_2_TEXT_Nocollegedegree + EDUCATION_2_TEXT_Collegedegree + EDUCATION_2_TEXT_Graduatedegree + DOB_AGE_BRACKET_45y + DOB_AGE_BRACKET_3545y + DOB_AGE_BRACKET_2535y + DOB_AGE_BRACKET_25y + AMBI_BIG5_Neuroticism + AMBI_BIG5_Extraversion + AMBI_BIG5_Openness + AMBI_BIG5_Conscientiousness + AMBI_BIG5_Agreeableness, data = df_covmis2)
## Coefficient covariances computed by hccm()
## Note: model has aliased coefficients
##       sums of squares computed by model comparison
res.aov
## ANOVA Table (type II tests)
## 
##                                        Effect DFn DFd      F        p p<.05
## 1                         EXPGRP_TEXT_Chinese   0 581     NA       NA  <NA>
## 2                           EXPGRP_TEXT_White   0 581     NA       NA  <NA>
## 3  CONTINENT_BORN_TEXT_1_CentralEasternEurope   0 581     NA       NA  <NA>
## 4       CONTINENT_BORN_TEXT_1_DeveloppingAsia   0 581     NA       NA  <NA>
## 5                   CONTINENT_BORN_TEXT_1_USA   0 581     NA       NA  <NA>
## 6         CONTINENT_BORN_TEXT_1_WesternEurope   0 581     NA       NA  <NA>
## 7               HH_INCOME_TEXT2_Lessthan30000   0 581     NA       NA  <NA>
## 8                HH_INCOME_TEXT2_30000to70000   0 581     NA       NA  <NA>
## 9                 HH_INCOME_TEXT2_70000ormore   0 581     NA       NA  <NA>
## 10                            SEX_TEXT_Female   0 581     NA       NA  <NA>
## 11                              SEX_TEXT_Male   0 581     NA       NA  <NA>
## 12           EDUCATION_2_TEXT_Nocollegedegree   0 581     NA       NA  <NA>
## 13             EDUCATION_2_TEXT_Collegedegree   0 581     NA       NA  <NA>
## 14            EDUCATION_2_TEXT_Graduatedegree   0 581     NA       NA  <NA>
## 15                        DOB_AGE_BRACKET_45y   0 581     NA       NA  <NA>
## 16                      DOB_AGE_BRACKET_3545y   0 581     NA       NA  <NA>
## 17                      DOB_AGE_BRACKET_2535y   0 581     NA       NA  <NA>
## 18                        DOB_AGE_BRACKET_25y   0 581     NA       NA  <NA>
## 19                      AMBI_BIG5_Neuroticism   1 581  0.445 5.05e-01      
## 20                     AMBI_BIG5_Extraversion   1 581 26.899 2.97e-07     *
## 21                         AMBI_BIG5_Openness   1 581 55.870 2.86e-13     *
## 22                AMBI_BIG5_Conscientiousness   1 581  0.233 6.29e-01      
## 23                    AMBI_BIG5_Agreeableness   1 581 22.109 3.22e-06     *
##         ges
## 1        NA
## 2        NA
## 3        NA
## 4        NA
## 5        NA
## 6        NA
## 7        NA
## 8        NA
## 9        NA
## 10       NA
## 11       NA
## 12       NA
## 13       NA
## 14       NA
## 15       NA
## 16       NA
## 17       NA
## 18       NA
## 19 0.000765
## 20 0.044000
## 21 0.088000
## 22 0.000401
## 23 0.037000
df_covmis2$demo_class %>% freq()
##     n    % val%
## 1 215 35.9 35.9
## 2 142 23.7 23.7
## 3  57  9.5  9.5
## 4   0  0.0  0.0
## 5  10  1.7  1.7
## 6 110 18.4 18.4
## 7  65 10.9 10.9

What if without conspiracy and xenophobia question

model <- '
Attention =~ covmis_att_flu + covmis_att_afrDie + covmis_att_eldrNoBgDl + covmis_att_rareNoWorr + covmis_att_bgThrt
Origin =~ covmis_orgn_covPlnnd + covmis_orgn_covNat + covmis_orgn_covNgeenLab + covmis_orgn_scntFkNwsCov
Politics =~ covmis_pltc_polBgDlIntrst + covmis_pltc_covNtSerPolSay + covmis_pltc_polDwnplCovPlpLDngr
Coverage =~ covmis_cvrg_mdiaCovBgrDl + covmis_cvrg_nwsGdJbComCov + covmis_cvrg_mdiaUseCovMkTrmpRepLkBd
AntiVacc =~ covmis_anti_frGovUseCovMndtVacc + covmis_anti_thnksNoCovVacc + covmis_anti_covVacEffRedVirus
CovidSkepticism2 =~ Attention + Origin + Politics + Coverage + AntiVacc
'
fit <- cfa(model, data = df_covmis, estimator = "ML")
## Warning in lav_object_post_check(object): lavaan WARNING: some estimated lv
## variances are negative
df_scorecovmis <- f_semscoring_latent(fit, df_covmis, scaled = F)
standardisation <- function(x){
  x <-  (x- (fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
  filter(!(rhs%in%lhs)) %>% 
  group_by(lhs) %>%
  summarise(sest=sum(est)) %>%
  inner_join(fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
    filter((rhs%in%lhs)) %>% 
    dplyr::select(rhs,est),
  by = c("lhs" = "rhs")) %>%
  mutate(factor=sest*est) %>%
  dplyr::select(factor) %>%
  colSums()))  / (fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
  filter(!(rhs%in%lhs)) %>% 
  group_by(lhs) %>%
  summarise(sest=sum(est)) %>%
  inner_join(fit %>% lavaan::partable() %>% as_tibble %>% filter(user==1) %>%
    filter((rhs%in%lhs)) %>% 
    dplyr::select(rhs,est),
  by = c("lhs" = "rhs")) %>%
  mutate(factor=sest*est) %>%
  dplyr::select(factor) %>%
  colSums() * 6) * 100
}
df_scorecovmis$CovidSkepticism <- df_scorecovmis$CovidSkepticism %>% standardisation
df_scorecovmis$CovidSkepticism %>% summary
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   9.774  18.800  21.822  30.392  76.180
ex <- car::powerTransform(df_scorecovmis$CovidSkepticism+0.0000001)
df_scorecovmis$CovidSkepticism^ex$lambda %>% hist()

df_scorecovmis$CovidSkepticismnorm <-  df_scorecovmis$CovidSkepticism^ex$lambda
df_covmis$CovidSkepticism2 <- df_scorecovmis$CovidSkepticism
df_covmis$CovidSkepticism2norm <-  df_scorecovmis$CovidSkepticism^ex$lambda
res.aov <- anova_test(CovidSkepticism2norm ~ EXPGRP_TEXT:CONTINENT_BORN_TEXT_1 + HH_INCOME_TEXT + SEX_TEXT + EDUCATION_2_TEXT:DOB_AGE_BRACKET + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, data = df_covmis)
## Warning: NA detected in rows: 43,60,106,112,310.
## Removing this rows before the analysis.
## Coefficient covariances computed by hccm()
## Note: model has aliased coefficients
##       sums of squares computed by model comparison
res.aov
## ANOVA Table (type II tests)
## 
##                              Effect DFn DFd      F        p p<.05   ges
## 1                    HH_INCOME_TEXT   7 608  0.390 9.08e-01       0.004
## 2                          SEX_TEXT   3 608  1.935 1.23e-01       0.009
## 3                  neuroticism_qual   2 608  1.491 2.26e-01       0.005
## 4                 extraversion_qual   2 608  4.211 1.50e-02     * 0.014
## 5                     openness_qual   2 608 17.502 4.07e-08     * 0.054
## 6            conscientiousness_qual   2 608  0.427 6.53e-01       0.001
## 7                agreeableness_qual   2 608 13.171 2.51e-06     * 0.042
## 8 EXPGRP_TEXT:CONTINENT_BORN_TEXT_1  17 608  2.760 1.88e-04     * 0.072
## 9  EDUCATION_2_TEXT:DOB_AGE_BRACKET  11 608  2.214 1.30e-02     * 0.039
res.aov <- anova_test(CovidSkepticism2norm ~ demo_class + neuroticism_qual + extraversion_qual + openness_qual + conscientiousness_qual + agreeableness_qual, data = df_covmis)
## Coefficient covariances computed by hccm()
res.aov
## ANOVA Table (type II tests)
## 
##                   Effect DFn DFd      F        p p<.05      ges
## 1             demo_class   6 645  5.136 3.61e-05     * 0.046000
## 2       neuroticism_qual   2 645  1.014 3.63e-01       0.003000
## 3      extraversion_qual   2 645  3.202 4.10e-02     * 0.010000
## 4          openness_qual   2 645 19.120 8.58e-09     * 0.056000
## 5 conscientiousness_qual   2 645  0.037 9.64e-01       0.000115
## 6     agreeableness_qual   2 645 13.577 1.68e-06     * 0.040000

##To do mediation analysis

write.csv(df_covmis,"./data/mjolnir_clean_v6_Covmis.csv")